The Geomatics and Landscape Ecology Research Laboratory at Carleton - - PowerPoint PPT Presentation

The Geomatics and Landscape Ecology Research Laboratory at Carleton University

Doug King, Geography and Environmental Studies Lenore Fahrig, Biology Kathryn Lindsay, Canadian Wildlife Service

Presented to the Canadian Remote Sensing Society, Ontario Chapter, April 5, 2005.

Presentation Structure

Overview of the lab Development context

Backgrounds of the co-directors Research issues and questions in landscape ecology related to habitat and species conservation Initial projects

Research directions

The Geomatics and Landscape Ecology Lab

The Geomatics and Landscape Ecology Lab

Remote Sensing Equipment and Data

Multispectral digital camera Spectroradiometer and targets

• E. Ontario Landsat data

High resolution satellite data

Field Equipment

Truck and car Hemispheric photography Trac Real-time differential GPS Laser rangefinders Other ecology

Objectives

• 1. Develop methods for

characterizing critical habitat required by species of concern (i.e., identify where critical habitat exists)

• 2. Develop models relating

species abundance and persistence to habitat availability across landscapes (i.e., estimate how much habitat is required).

Backgrounds of the Co-directors

• D. King: Research Program

Develop remote sensing methods for evaluation and monitoring of forest and wetland composition, structure, and health at local to regional scales

Sensor Research

1983-90: Video sensor 1990: Kodak Megaplus 1991-00: Kodak DCS CIR 1993-99: Digital Camera

1300 x 1024 pixels 8-bands, 10nm bandwidth

2002+ : Duncantech MS3100 and 4100

Evaluation of Forest Structure Condition at an Acid Mine Site

1993-2003 with: J. Levesque, N. Walsworth, E. Seed, I. Olthof, C. Butson,Y. Cosmopoulos

In boreal forest adjacent to 350ha of acid tailings

Runoff pH = 1.4 - 2 High metal concentrations

Landsat

• 1. Growth-mortality analysis of

individual trees using archived air photos (1961 – 1991)

Trend of increased forest

• pening within 200m of tailings,

with poor regeneration Beyond 200m from tailings, forest is more stable; gradual infilling of conifers

Digital camera with filter wheel: 10 nm bandwidths

• 2. Multiple regression modelling of forest structure

and health

Best image predictors of forest variables are spatial

• Semivariance range of image brightness and of image fractions

(vegetation, shadow, wood)

• Texture, particularly for individual crown health

Additional spectral information in models

• NDVI
• Shadow and sunlit crown fractions
• Brightness of shadow fraction more robust with view angle than

shadow fraction itself for modelling LAI e

• 3. Development of a Multivariate Health Index

Canonical Correlation showed:

Decreased LAI and Cover % + increased blow down and standing dead forest volume

significantly associated with

Increased image texture, texture variation, and crown edge shadow + decreased deep shadow, sunlit crown and NIR brightness. Used 1997 index to predict 1999 condition

Stable and improved condition well predicted Deteriorated forest close to tailings (more open) not well predicted

Ice Storm Damage Modelling and Mapping

40 - 110mm of freezing rain across E. Ontario, S. Quebec, and NE. U.S.

Site 1: Local Damage and Structure Modelling

1998 + with: E. Seed, P. Pellikka (PDF), R. Bemrose

Conducted similar modelling of structure and health as at mine site Kodak DCS 460 CIR

Current Studies

Temporal analysis of vertical forest structure in relation to damage

Up to 12m from hemispheric photos

Evaluation of the impact on biophysical models of ortho-rectification, topographic brightness, BRDF and

• ptical corrections

Site 2: Regional Damage Mapping

1998-2002 with I. Olthof and OMNR

Plot Satellite model Other environmental + +

+

Damage map

Damage 0-25% 25-50% >50%

• Compared 4 classifiers:

NN performed best

• Used pre and post storm

Landsat, distance to forest edge, elevation, ice accumulation.

Forest Regeneration Assessment

1996+ with: D. Pouliot, K. Haddow, OMNR, CFS

Includes

Automated conifer (crop) tree detection, delineation and measurement Modelling the amount of competing deciduous vegetation Species classification

Study Site 1

Kodak DCS 420 CIR 2.5cm pixels OMNR/CFS regeneration experiment Sault Ste. Marie, Ontario

Tree Detection-Delineation Algorithm

First developed using this well controlled experimental set-up Apex Detection Automated delineation (yellow) vs manual (blue) Spruce Pine

Site 2: Operational Cutovers

Apply detection-delineation algorithm to operational regeneration

Three sites of varying competition

Develop method for leaf-off competition assessment

Based on thresholds of woody stem density used in silvicultural treatment decisions

Duncantech MS3100 images of field plots with conifers delineated

Imagery and Tree Delineation

Camera mounted at end of boom

Site 3: Fallingsnow

OMNR led, with D. Pouliot

Control: no treatment Herbicide (Vision) treatment

• L. Fahrig Research Background

Pioneered spatial simulation modelling of population dynamics

Includes intensive field study for model development and validation

Conducts multi-landscape, multi-scale empirical analysis to assess hypotheses of species response to landscape structure and composition

Studies abundance, distribution and persistence of organisms

Co-founder of the J. Conservation Ecology

• L. Fahrig (cont’d)

Current research interests: Impacts on population distribution or species richness of:

Landscape fragmentation Increased road and traffic density

• Includes determining which species are most vulnerable to roads, and

what road patterns are least damaging to wildlife populations

Other disturbance types

Relations between organism dispersal behaviour and landscape structure

• K. Lindsay Research Background

Pioneered landscape level studies in agriculture of effects of herbicides on bird abundance and species richness Previous team leader in Terrestrial Ecology Research at EPA Member of SARA advisory teams for various species Adjunct professor in Biology and Geography-Env. Studies Conducts research in

Population viability analysis Population modelling using environmental and other variables Biodiversity conservation network design

Includes research on socio-economic role of landscape ecology in conservation and resource management policy

General Research Approach

Research Issues

Over-arching Research Question

How do species respond to landscape structure and disturbance? Example responses:

Abundance, or presence/absence

• f a given species, its predators, or a pathogen that affects it.

Small mammal movement success

Each response needs to be analyzed spatially, and often temporally Need geospatial information on landscape composition and structure

Examples: Forest and open habitat composition and cover Road and traffic density Landscape heterogeneity and fragmentation Density of forest/agriculture edge

Typical Landscape Ecology Design

landscapes landscape scales focal point

• 3
• 2
• 1

1 2 3 4 2.8 3.2 3.6 4 4.4 4.8

Log (Traffic Density) Residual Standardized Leopard Frog Population Abundance

Leopard Frog Abundance at Focal Ponds vs. Traffic Density in Landscapes within 1.5 km of Focal Ponds

Species response variable(s) (e.g., abundance) measured at focal points (e.g., ponds, forest sites) Landscape predictor variable(s) (e.g., traffic density) measured at multiple scales in landscapes around each focal point

Example Landscape Ecology Study

To test hypothesis, need geospatial data:

number of large dead trees within 1 km of each raccoon sampling point landscape maps that differentiate cover by potential raccoon food

• (grain fields) vs. other open habitats (forage

crops, grazed lands)

Effect of Landscape Structure on Raccoon Occurrence

Peak occurrence found in moderate forest cover within 1 km of raccoon sampling sites Hypothesis: due to balance between availability of den sites (e.g. hollow dead trees) and availability of food (e.g. corn fields).

forest cover in landscapes within 1 km of raccoon sampling sites raccoon occurrence

Initial Collaborative Projects in the Lab

Hooded Warbler Habitat Mapping

with J. Pasher, K. Lindsay

Hooded Warbler found only in SW Ontario Field measured association of forest gaps with nesting sites

Non nest site Nest site

Hooded Warbler Habitat Mapping

Landsat images for June and early September

Non-forest masked out Relatively calibrated

Significant differences in image:

spectral brightness textures fractions of shadow, soil and coniferous

Classification of forests of SW Ontario

Patterns of known nest and non nest areas well classified in focus forest Validation nest areas throughout SW Ontario well classified (> 80% accuracy) Validation non-habitat areas not well classified

Landsat 5 Habitat Nests Non nest areas

Wetland Classification and Productivity Mapping

with K. Dillabaugh, K. Lindsay, L. Fahrig

Riparian marsh classification and biomass modelling using Ikonos

Butterfly Occurrence Prediction using Environmental Modelling

with T. Nguyen, K. Lindsay

Model occurrence of butterfly species in the A2A corridor using climate, topographic, and land cover data.

Enforcement in National Wildlife Areas

Led by NWRC, with R. Bemrose 2001 Landsat Ikonos Cranberry farm 1989 Landsat Portobello Creek, NB

Research Directions for the Lab

Currently: Merging grad students and researchers from our individual groups Developing projects for incoming grad students that are collaborative and integrate geomatics with landscape ecology and wildlife analysis Defining a strategic plan for the lab

Umbrella research theme Collaboration with others outside the lab Considering production oriented work to partially fund lab maintenance and upgrades

Example Research Theme

One theme being considered:

Ecological Impact of Landscape Change in E. Ontario

Lag in ecological response to disturbance Need temporal analysis over last 50+ years

Includes archived air photos and maps, old and recent satellite data

Scale(s) of analysis will depend on the research questions

e.g. Studies Local impacts of roads and fences: very high resolution Relations of pond vegetation to amphibian populations: high resolution Regional impacts of wetland loss and forest change: moderate resolution

Many of these will involve scaling from local to regional.

Example study: Leopard Frog Response to Landscape Change

Known from previous research that within 1.5 km

• f focal ponds:

Abundance increases with increased breeding habitat (ponds with emergent vegetation) and feeding habitat (meadows) Abundance decreases with increasing traffic density

Question: How quickly do populations decline in response to changing landscape structure? Need geospatial data (breeding habitat, feeding habitat, and traffic density) extending back about 70 years

Summary

Geomatics and Landscape Ecology Laboratory opened in November 2004, merging the labs of D. King, L. Fahrig, and K. Lindsay Integrates geomatics (King) with landscape ecology (Fahrig) and population analysis / species conservation (Lindsay) Research direction: ecological response to landscape dynamics

Studies will be

• species driven (e.g. SARA, or other indicator species)
• eco-type driven (e.g., mapping/monitoring of given ecological cover type), or
• Analytic process driven (e.g., species distribution modelling for conservation design)

All of these require geospatial data at certain scales and times.

Acknowledgements

Funding and Support NSERC, US National Geographic Society, OMNR, CFS, CWS, CFI, OIT, R. Hamlin Other support CCRS, Buchanan Forest Resources Inc., NCC