landscape scale patterns of canopy gaps in the old growth...
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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
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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
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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)
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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
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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
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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
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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)
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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
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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
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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
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Orthoimage
Land Cover map
RESULTS
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Canopy Gaps MapRESULTS
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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
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Landscape scaleRESULTS
LARGEST GAPS
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Gaps spatial pattern (Ripley’s K)RESULTS
RESERVEClustered in the Reserve
Clustered in the buffer zone
BUFFER ZONECORE AREA
Random within the core area
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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
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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
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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.
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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
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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/