1. Modeling biodiversity response to habitat heterogeneity in agricultural lands of Eastern Ontario using multi spatial and temporal remote sensing data Supervisor: Dr. Doug King Niloofar Alavi

2. Background: Biodiversity and Habitat Heterogeneity  Biodiversity:  The variability among living organisms from all sources including, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part -International Convention on Biodiversity (1992)  Biodiversity has declines due to human activities such as intensive agriculture  Landscape heterogeneity:  Variation in the horizontal dimension of the landscape -August (1983)  Compositional and configurational heterogeneity  Landscape Heterogeneity Hypothesis:  heterogeneous and complex habitats promote biodiversity by providing more available sources for species -Simpson (1949) -MacArthur & Wilson (1967)

3. Using Earth Observation Data in Biodiversity Modeling  Spatial resolution  Temporal resolution  Spectral resolution  Spectral Heterogeneity  Discrete thematic maps vs. continuous metrics

4. Research Questions and Goal  What is the response of biodiversity to habitat heterogeneity at multiple spatial and temporal scales in mixed agricultural landscapes?  Can high spatial and low temporal resolution imagery, and low spatial and high temporal resolution imagery be used to create robust biodiversity models that reflect the response of biodiversity to habitat heterogeneity?  To fill out the gap in the literature by creating a multi-spatial and multi-temporal biodiversity model using multiple farmland taxa.

5. Study Site  An agricultural region within Eastern Ontario  Approximately 15,000 km 2  Mainly covered by maize (21%), soybean (19%), forage crops (alfalfa, clover, hay; 30%), and wheat (3%)  Approximately 100 1 km 2 sample landscapes  low crop diversity and low mean field size  low crop diversity and high mean field size  high crop diversity and low mean field size  high crop diversity and high mean field size

6. Study Site Pasher et al., 2013

7. Data  Biodiversity Data:  surveyed in the cropped portions of 93 of the sample landscapes  46 landscapes were surveyed in 2011  47 landscapes were surveyed in 2012  seven species group: birds, plants, butterflies, syrphids, bees, carabids and spiders  Alpha and Gamma indices Girard et al., 2012

8. Data  Remote sensing data  Time series of:  MODIS  1999  MODIS 16-day and 7-day NDVI  Landsat  1982  40-cm resolution aerial photos (2011-2012)

9. Biodiversity Modeling Variables  Discrete variables:  Compositional  Crop type variability  Patch richness  Amount of suitable species habitat  Amount of natural and semi-natural patches  Configurational  Mean patch size  Edge density  Mean patch shape variability

10. Biodiversity Modeling Variables  Continuous remote sensing variables  Original spectral band reflectance,  Band combinations  Vegetation indices  Band transformation  Principal Component Analysis  Tasseled Cap Transformation  Fractions  Spectral Mixture Analysis

11. Temporal Analysis  Coarse Scale Temporal Analysis  MODIS  within seasonal  Fine Scale Temporal Analysis  Landsat  inter-seasonal and inter-annual

12. Expected Results  We expect to find a correlation between the landscape heterogeneity metrics and the response of different species groups.  We expect that the biodiversity models derived from the selected continuous landscape metrics can display this correlation and create robust models that explain the response of different taxa to these metrics.  We expect that MODIS and Landsat time series will be able to detect the temporal trajectories of past phenological changes in mixed agricultural landscapes.  We expect to identify the optimal operational and conservational scale at which each species group responses to habitat heterogeneity in mixed agricultural landscapes.

13. Questions and discussion

14. Spectral mixture analysis  In spectral mixture analysis the spectral signatures of the constituent substances present in a mixed pixel are referred to as endmembers, and the fractional area coverage of each endmember in a pixel is called its abundance.  spectral mixture analysis is the process of decomposing the acquired spectrum of a mixed pixel into a set of endmembers and their corresponding factional abundances  3 directions of spatial spectral mixture analysis:  Endmember extraction  Directly from the remote sensing images (image endmember)  Measure in the field or laboratory (reference endmember) ×  Selecting endmember combination  Limitations:  The spectral signature of each endmember is assumed unchanged: Endmember variability problem  The number of endmembers in the entire scene is assumed unchanged: Too many endmemebers for each pixel causes error  Methods:  Per-pixel  Per-field  Abundance estimation  Methods:  Linear mixture model: spectral signature of a mixed pixel is represented by the weighted sum of the endmember spectra and that the weights associated with the endmembers are given by their corresponding proportional area coverage in the pixel.  Non-linear mixture model: When the ground materials depict an intimate mixture where the light is multiply scattered between at least two components.  The importance of extracting exact number of endmembers  Too few endmembers  Too many endmember s

15. Band transformation  Principal Component Analysis  A linear transformation technique  The original set of potentially correlated numerical variables with high covariance  smaller and uncorrelated sets of variables  Reduces redundancy  In mixed landscapes usually the first three principal components represent most of the variance of the original dataset.  Measuring the mean Euclidean distance between spectral clusters derived from PCA is a measure of spectral heterogeneity.

16. Band transformation  Tasseled Cap Transformation  A linear transformation technique  The original spectral bands  new sets of bands  The first three TCTs :  Brightness  Greenness  Wetness  The Brightness component is by definition a positive value,  The Greenness depends on the contrast between the visible and near-infrared

17. Scale in modeling biodiversity  Biodiversity: various forms of life on earth.  genetic  Species  species richness  species evenness or abundance  Ecosystems  Habitat heterogeneity  Compositional heterogeneity  Configurational heterogeneity  Habitat heterogeneity hypothesis  Heterogeneity vs. fragmentation  Extinction threshold  Intermediate heterogeneity hypotheses Fahrig et al. 2011 Fahrig et al. 2003

18. Species-specific factors  Some studies noted species-specific factors that affect the biodiversity response to habitat heterogeneity.  Species mobility  Oliver et al. (2010)  36 British butterfly species  166 study sites  3 different spatial scales:  1 km  2 km  5 km from the center of the study sites.  Trophic level  body size  habitat specialization  Steckel et al. (2014)  bees, wasps and their antagonists  3 regions in Germany  Local scale (sampling plots)  Landscape scale (8 radii between 250 m to 2000 m around the sampling plots)  Regional scale (each study region).

19. Biodiversity responses to habitat  Loss of species  Species replacement  Stability in species assemblage Burel et al. 2004

20. Spatial and Temporal Scales in Biodiversity Modeling  Spatial scale:  Extent  Grain  Spatial scales in modeling biodiversity in agricultural landscapes:  between-farms (regional)  between-fields (landscape)  within-field (local)  Spatial-temporal trade-off Turner et al. 2001

21. Landscape matrix vs. patchiness  Landscape heterogeneity:  The variation in the horizontal dimension of the landscape (August, 1983).  1) Landscape matrix  landscape is divided into:  “habitat”  “non-habitat”  2) Heterogeneous landscapes  This view has been challenged by many authors:  The species perceive the landscape in a much more complex manner and they use more than one land cover type as resources to provide their requirements  The heterogeneous landscape view replaced the habitat matrix view to explain the complexity of species response to habitat patchiness. Fahrig et al. 2011