Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger

National Park, South Africa

Sandra MacFadyen 1

1 PhD student and GeoSpatial Analyst, South African National Parks (sandra.macfadyen@sanparks.org)

Hui C 2 and Verburg P 3

2 Supervisor, Stellenbosch University, Department of Botany & Zoology (chui@sun.ac.za)3 Co-supervisor, Vrije University, Amsterdam, Environmental Studies (peter.verburg@ivm.vu.nl)

Research Unpacked

• Linking long-term patterns of landscape heterogeneity to changing ecosystem processes in the Kruger National Park, South Africa

Landscape Heterogeneity

• Landscape heterogeneity is the cause and consequence of interactions between spatial patterns and ecological processes (Turner et al 2001).

• Heterogeneity is the measure of the degree of difference between different landscape elements.

composition1 (type); structure2 (pattern) and function3 (process)

MacFadyen 2010

Functional Importance

• Spatial heterogeneity at a variety of scales is functionally important (Pickett et al 1999)

• Without an adequate understanding of natural pattern and process, protected area managers are flying blind (Olson 2010)

….Pattern = Process = Pattern….

• With the understanding that spatial patterns affect ecological processes, which in response affects spatial patterns, the natural spatial patterns of the heterogeneity should guide management decisions in protected areas rather than unnatural administrative boundaries (Leitão et al 2006)

• Use pattern to decipher process

Bailey 2009

Research Objectives

1. ID patterns of heterogeneity at different scales.2. ID processes responsible for these patterns.3. Investigate dynamics of pattern and process.4. Management implications.

OBJECTIVE 1ID landscape heterogeneity patterns ∆ scales

19722010 OBJECTIVE 3Dynamics of Pattern & process

OBJECTIVE 2ID processes <=> Patterns

OBJECTIVE 4Management Implications

South African National Parks

Mabunda et al. 2003

INTRODUCTION

Kruger National ParkINTRODUCTION

History of ChangeINTRODUCTION

Chapter 1• research questions

– Can Landsat-MSS -TM and -ETM+ data be satisfactorily geometrically and radiometrically intercalibrated for standardized comparison?

– Does auxiliary data influence pattern detection [physical landscape e.g. topographic elements (elevation, aspect, slope), geology and climate]?

– What are the underlying patterns of landscape heterogeneity?– What contribution do landscape metrics make to the pattern and process question?– What is the influence of scale on the detection of landscape heterogeneity?

• variables or indices– Landscape structural (spectral) heterogeneity– Landscape functional (metrics) heterogeneity

• data requirements– x2 (summer and winter) to x4 (summer, autumn, winter, spring) images per year between 1972 and 2010 = 76-152

images– DEM, geology, rainfall

• basic methodology– Real world = raster image (landsat)– geometric & radiometric correction = standardize Landsat MSS-TM-ETM (38yrs)– Band spatial autocorrelation = degree of spatial dependence = appropriate band combinations– Classify (unsupervised, object-orientated, conditional entropy) @ varied scales = test sensitivity of classifications – Integrate auxiliary data (elevation; slope; aspect; geology and rainfall)? – test SAC– Choose best fit (how?)– Calculate structural heterogeneity using moving window or multi-scale heterogeneity maps – Calculate landscape metrics (number of patches, average patch size, total edge density, double-logged fractal,

contagion, aggregation index, interspersion/juxtaposition, patch shape variability, entropy, proximity and nearest neighbour distances)

– Calculate functional heterogeneity using moving window or multi-scale heterogeneity maps • expected results

CHAPTER 1

What constitutes a LandscapeCHAPTER 1

What constitutes a LandscapeLandform (geology + topographic elements) +> climate <=> ecological processes <=> vegetation and animal response <=+> disturbance

Wiens (1999)

LANDFORM CLIMATE+

elevation

slope

aspect

geology

regime

local weather

microclimate

MOVEMENT OF WATER

SOILHABITAT

HABITAT

Landscape Schematic

LANDFORM CLIMATE+

elevation

slope

aspect

geology

regime

local weather

microclimate

MOVEMENT OF WATER

FLORA FAUNA

SOILHABITAT

HABITAT HABITAT

HABITAT

LANDFORM CLIMATE+

elevation

slope

aspect

geology

regime

local weather

microclimate

MOVEMENT OF WATER

FLORA FAUNA

SOIL

DISTURBANCES

 

HABITAT

HABITAT

HABITAT

HABITATHABITAT

• Topography• Geology• Soil• Rainfall• Temperature• Flora• Fauna

CHAPTER 1

LANDSAT ETM+10 May 2000

False-color composite

LANDSAT ETM+10 May 2000

False-color composite

LANDSAT ETM+10 May 2000

False-color composite

LANDSAT ETM+10 May 2000

True-color composite

LANDSAT ETM+10 May 2000

Panchromatic

Limitations of Data

• Scale: Extent and Resolution• Horizontal and Vertical structure

CHAPTER 1

Difference of ScaleElephant

VS.

Elephant Shrew

CHAPTER 1

Horizontal and VerticalCHAPTER 1

Chapter 2• research questions

– ID relationship between selected processes and the structural patterns of landscape heterogeneity.– ID relationship between selected processes and the functional patterns of landscape heterogeneity.– What are the primary processes of landscape change in the KNP?

• variables or indices– Correlation coefficients for derived landscape structural heterogeneity and ecological processes.– Correlation coefficients for derived landscape functional heterogeneity and ecological processes.

• data requirements– Ch1 derived Landscape structural (spectral) heterogeneity– Ch1 derived Landscape functional (metrics) heterogeneity – Selected physical (fire), chemical (nutrients) and biological (animal movement) ecological processes

• basic methodology– Identify processes (drivers of or responders to) of landscape change by exploring the relationships

between landscape heterogeneity patterns and -herbivore response; -fire and -rainfall patterns.– Using General Linear and General Additive Models and test Neutral Landscape model and

Geographically Weighted Regressions.– Test spatial auto-correlation

• expected results

CHAPTER 2

Exclusion Experiments

• Inside vs. Outside: What is different/missing?

CHAPTER 2

Chapter 3

• research questions– Are KNP landscapes homogenising or diversifying over the last 38 years?– What are the spatial and temporal patterns of heterogeneity change?• variables or indices • data requirements– Ch1 derived Landscape structural (spectral) heterogeneity– Ch1 derived Landscape functional (metrics) heterogeneity • basic methodology– Automate processing of imagery according to results of ch1– Quantify differences between seasons, years, decades using Renyi’s generalized parametric diversity

function - landscape spatial dynamics and/or Object-oriented and a chi-square transformation change detection algorithms - assess spatial changes in heterogeneity at different scales over time and/or R and the BFAST library – characterize change by both magnitude and direction.

– Landscape trend analysis?• expected results– Change is either directional

CHAPTER 3

LANDSAT ETM+False-color composite

2000

LANDSAT TMFalse-color composite

1984

Chapter 4

• research questions– management implications– How will the identification of drivers of landscape change,

influence protected area management and decision making.– How can this help global conservation efforts?– Where is the most change occurring?

• variables or indices • data requirements• basic methodology• expected results

CHAPTER 4

Application of Results

• Philosophically• Theoretically• Practically• KNP management plan

CHAPTER 4

Schedule / TimelineActivity Target CommentsLiterature review Continuous Read Read ReadData collection Dec-11 SANSA

Data processing May-12 Investigate different geometric and radiometric correction techniques

Data exploration Jul-12 Explore different ideas of how to model heterogeneity

Data analyses: Objective 1 Oct-12 Identify structural patterns of landscape heterogeneity at different scales.

Journal paper 1 Jan-13 Draft paper for publication

Data analyses: Objective 2 Mar-13 Identify processes responsible for these patterns.

Journal paper 2 Jun-13 Draft paper for publication

Data analyses: Objective 3 Jun-13 Investigate the dynamics (occurrence and interaction) of both pattern and process over the past 38 years.

Journal paper 3 Aug-13 Draft paper for publication

Data analyses: Objective 4 Nov-13 Identify responders to landscape change (pattern + process).

Journal paper 4 Feb-14 Draft paper for publication

Data analyses: Objective 5 Apr-14 Identify drivers of landscape change and translate results into possible management actions.

SANParks report Jun-14 Final project reportPhD thesis Dec-14 Compile papers into final thesis

Thank you

Questions?

Notes to myself

• Be clear about what elements of landscape heterogeneity are being measured• What metrics and why. How will I decide what indices prove useful and how will I know if a

changed index is important to ecosystem functioning.• Develop causal diagram to explain how factors interact, how will I investigate relationships and

what data to use• Be clear about auto-correlation and spatial variability (e.g. within satellite image)• Be more specific about scale (explain extent vs. grain)• Stress natural systems when talking about ecological importance of heterogeneity (e.g.

fragmentation=bad)• Be clear about what aspects of function will be addressed• NB to explain and defend image classification technique and add sensitivity tests• Can I test the validity of the statement, “ greater landscape heterogeneity provides increased

ecosystem resilience and higher species richness”?• Add general explanation of landscape trend analysis• NB to explain why each time I describe how i.e why a certain technique/statistic