Modelling forest structural complexity as an indicator of biodiversity using airborne remote sensing and topographic information.
Jon Pasher
1
and Dr. Doug J. King
1
1
Department of Geography and Environmental Studies, Geomatics and Landscape Ecology
Laboratory, Carleton University. 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S5B6
1-613-520-2600 x8439; jpasher@connect.carleton.ca, doug_king@carleton.ca
1.0
Introduction and Objectives
It has been found that biodiversity and species distribution are significantly affected by the spatial structure of the landscape. The habitat heterogeneity hypothesis summarizes the relations between ecosystem structure and biodiversity levels at multiple scales, including within individual forests. This hypothesis states that more complex areas within a forest should exhibit greater biodiversity as a direct result of the greater number and variety of habitats available which species can use to exploit the environment, compared to structurally simpler areas (MacArthur and MacArthur 1961; Bazzaz 1975; McElhinny et al. 2005). This research attempted to exploit this relation by using structural complexity, defined as the abundance and variability of horizontal and vertical structural attributes present in the forest (McElhinny et al. 2005) as an indicator of habitat and biodiversity.
The primary objective of this research was to develop a multivariate structural complexity index in a temperate hardwood forest based on modelling the relationships between field structural variables and geo-spatial data, including image variables extracted from high resolution remote sensing along with topographic information. This objective stems directly from the need for methods for assessment of forest biodiversity, and in particular, the need to develop spatially continuous and extensive indicators of biodiversity for use in modeling, mapping and monitoring.
2.0
Methods
2.1
Study Area
The research was carried out in Gatineau Park, which is located in southern Quebec, Canada.
More than 80% of the park’s approximately 36 000 hectares is forested, with approximately 55% dominated by hardwoods. The dominant overstory species in the study area was sugar maple
( Acer saccharum Marsh.), but there were small patches dominated by American beech ( Fagus grandifolia Ehrh.), trembling aspen ( Populus tremuloides Michx.), and red oak ( Quercus rubra
L.) (King et al. 2005). A long strip traversing the park was selected for this study (~ 180 ha)
(Figure 1) where previous modelling work had been carried out (King et al. 2005), and which contained a range of elevations, slopes, and aspects. It had also experienced significant patchy damage from a severe ice storm in 1998 that provided a range of structural conditions.
2.2
Field Data and Structural Measurements
Based on preliminary qualitative assessments of overstorey tree size and position, canopy closure, understorey and ground vegetation, as well as the amount of deadwood, fifty field plots
(each 400 m 2 ) were established to represent the range of structural and topographic conditions found across the study area. A set of twenty-four field variables were measured and derived within each of the field plots in order to represent different aspects of the forest structure.
Measures of overstorey tree size and horizontal distribution, ground and understorey vegetation abundance, canopy closure, and dead wood presence were taken within each plot, with average
IUFRO Division 4 Extending Forest Inventory and Monitoring
May 19-22, 2009, Quebec City, Canada

and variance calculated to represent different aspects of the horizontal and vertical structure of the forest. Together these variables (summarized in Table 1) were used to summarize the overall structural complexity (McElhinny et al. 2005). The individual variables were selected based on their use in previously published studies of structural complexity indices, and many were selected specifically based on their known association with biodiversity, or because of their direct or indirect manifestation in the imagery.
Figure 1.
Location of study area within Gatineau Park near Ottawa, Canada.
Table 1.
Field variables measured and derived from plots representing the various aspects of structural complexity in the forest.
Structural Attributes
Measurements and Sampling
Methods
Derived Field Plot Variables
Overstorey trees
(> 10 cm dbh) dbh (cm); height (m); crown diameter (m)
# of trees; # of large trees (> 35 cm dbh); x,¯ and S.D. for each plot; live basal area (m 2 /ha)
Tree spacing
Canopy openness and
LAI
Ground and understorey vegetation
Nearest neighbour (NN) distance
(m) between overstorey trees
Hemispherical photographs
(height 1 m and 6 m)
5 classes, along diagonals every 50 cm: < 10 cm, 11 – 50 cm, 51 cm – 1 m, 1 – 2 m, and > 2 m x,¯ for each plot
Openness ( 0° – 60°) (%) and LAI (55° – 60°) derived for each height
% of plot for each class
Snags Presence; dbh (cm); height (m)
# of snags; x,¯ and S.D. for each plot; dead basal area (m 2 /ha)
Coarse woody debris
(CWD)
(> 7.5 cm diameter)
Crossing plot diagonals # of pieces of CWD
IUFRO Division 4 Extending Forest Inventory and Monitoring
May 19-22, 2009, Quebec City, Canada

2.3
Image (Predictor) Variables
Colour infrared (CIR) imagery was acquired over the study area on August 21 st 2007 with 20 cm nominal ground pixel size. Following brightness corrections, the images were mosaiced together and georeferenced to existing 25 cm pixel orthophotos.
A variety of image variables were calculated for each field plot directly from the airborne imagery with the objective of differentiating levels of structural complexity, with the overall hypothesis that more complex areas would exhibit greater variation in the imagery. A total of eighty-six variables were tested in this analysis, representing different spectral, spatial, and object-based aspects of the imagery. As well, since the relative position in the landscape can have an impact on forest structural conditions, topographic information (elevation, slope, aspect, and solar illumination) was extracted from a 10 m cell size digital elevation model.
2.4
Modelling Structural Complexity Using Redundancy Analysis
Redundancy Analysis (RDA) was used to develop a multivariate model of structural complexity that could be explained by the geospatial information. RDA is a statistical technique capable of describing relations between multiple explanatory / predictor variables (image and topographic variables) and multiple response variables (structural measurements) (Legendre and
Legendre 1998; ter Braak and Smilauer 2002). Bootstrapping was utilized (with a 40:10 split of field plots) in order to build models using stepwise selection, and, in a second stage, to test each individual model that was created. Each stage of bootstrapping was carried out using fivehundred iterations. The models were examined in terms of the number of predictors included, the average Adj. R
2
, and the average AIC summarized over five-hundred iterations. As well, for each iteration in the second stage, the ten withheld field plots were used to validate each model by calculating RDA
Observed
(using the field variables) and RDA
Predicted
(using the predictor variables) and comparing the two calculated matrices or sets of canonical variables (Legendre and Legendre
1998). The final selected model was run using all fifty field plots in CANOCO 4.5 (ter Braak and
Smilauer 2002), which provided the desired numerical and graphical outputs, including field variable scores (“species scores” in the software terminology) and “bi-plot scores of environmental variables” (predictor variable scores) as well as an ordination tri-plot for visualization of variable relationships.
3.0
Results
The structure found in the field plots was quite varied and represented well the conditions in the study area forest. The bootstrapping procedure used for the selection of predictor variables resulted in five-hundred models, which provided a range of explained field data variance and included a variety of predictor variables. The final model that was selected, based on the derived statistics for each model, and focusing on minimizing the number of predictor variables, had an adjusted R
2
of 0.35 (p < 0.001) for the first RDA axis. This axis was defined as a Structural
Complexity Index (SCI) based on its representation of a structural complexity gradient defined by the field measurements. The second phase of bootstrapping for this model showed the average
RMSE of the model to be 19.9 %. In the tri-plot produced from this model (Figure 2), the SCI (x axis) was interpreted as showing increasing complexity represented by increasing canopy openness, increasing understorey vegetation (> 2 m), increasing snag size and amount of CWD, increasing overstorey tree size and variation (dbh and height), and increasing distance between trees.
IUFRO Division 4 Extending Forest Inventory and Monitoring
May 19-22, 2009, Quebec City, Canada

Figure 2.
Tri-plot for final RDA model. The 1 st axis (X axis) represents the structural complexity index (SCI) showing a gradient of structural complexity in the forest from low (left) to high (right). Colored points represent the fifty field plots with their qualitatively associated complexity level.
In terms of the image predictor variables included in the final model, increasing structural complexity manifested itself as: i) increasing within-crown brightness variability, perhaps caused by small gaps and dead branches within crowns (IM1 and IM2), ii) increasing within-shadow
NDVI (IM3), which was linked to understorey vegetation within canopy gaps, and iii) decreasing within-shadow texture (IM4), again reflective of the thick understorey vegetation present in complex areas. As well, although not strongly related to the general complexity gradient represented by the first RDA axis, increasing structural complexity was found to increase with elevation (IM5). The second axis (y axis) was mostly composed of elevation information, perhaps representing a weak but real association between forest structure and elevation in the forest (King et al. 2005), however this axis only contributed 6 % of explanation (p = 0.080).
IUFRO Division 4 Extending Forest Inventory and Monitoring
May 19-22, 2009, Quebec City, Canada

4.0
Discussion and Conclusions
The results were considered successful compared to other published structural complexity modelling studies (eg. Coops et al., 1998; Olthof and King, 2000; Cosmopolous and King, 2004; de la Cueva, 2008) and the methods presented are thought to be useful for deriving spatially continuous and extensive indicators of habitat and biodiversity within a temperate hardwood forest. This research is part of a larger project to develop spatially extensive remote sensing based indicators in order to augment point / plot-based field surveys, including detecting and mapping of dead trees across forests, which are considered important structures for maintaining biodiversity. In related research the SCI model was applied spatially and validated through independent field site visits to areas of predicted very high and very low complexity. Further research applying these methods in different forest types and adaptation of the methods to high resolution satellite imagery, which can provide much greater coverage compared to airborne imagery, is currently underway.
In general, the methods presented can potentially provide forest managers with a means to map and monitor forest structural complexity for input to biodiversity conservation and forest management planning.
Acknowledgements
This research was funded by an NSERC Discovery grant to D. King. The Canada Foundation for
Innovation, the Ontario Innovation Trust, the National Wildlife Research Centre and private sources funded Carleton University’s Geomatics and Landscape Ecology Laboratory, which provided all the necessary field equipment and infrastructure used in this research. The authors are grateful to the NCC for access to Gatineau Park and for providing geospatial data used in this research. Many thanks to C. Czerwinski, M. Leni, V. Torontow, and R. Bemrose for help in the field. The continuous feedback and advice of I. Olthof (CCRS), E. Humphreys (Carleton U.) and
J. Kerr (U. of Ottawa) has been greatly appreciated. Finally, the intensive RDA modeling support and advice of R. Proulx (Max Planck Institute, Germany) is greatly appreciated.
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IUFRO Division 4 Extending Forest Inventory and Monitoring
May 19-22, 2009, Quebec City, Canada