The potential of landscape metrics from Remote Sensing data as indicators in

forest environments

Niels Chr. Nielsen, M.Sc.

niels_c_nielsen@get2net.dk

Intro

Lancaster University thesis under way:Development and test of spatial metrics derived from EO data for indicators of sustainable management of forest and woodlands at the landscape level

JRC Project:Development and evaluation of remote sensing based spatial indicators for the assessment of forest biodiversity and sustainability, using landscape metrics derived from high- to medium resolution sensors

NordLaM Nordic Workshop: Deriving Indicators from Earth Observation Data - Limitations and Potential for Landscape

Monitoring, 22nd - 23rd October, Drøbak, Norway

Structure of presentation:

Definitions of indicators for different purposes

Landscape ecology – spatial metrics

Land Cover and forest maps, data needs and potential outputs

Processing chain, combining with GIS

Limitations to monitoring, examples from study of fragmentation

Conclusions, perspectives for monitoring

Helsinki (93) – Lisbon (98): Ministerial Conference on Protection of Forests in Europe

Convention on Biological Diversity

IUFRO working group on Sustainable Forest Management (SFM)

European Landscape Convention (Firenze 2000)

“Natura 2000” network (linked to the EU habitats directive)

Activities somehow related:Timber Certification

BEAR project on forest biodiversity + indicators of same

GAP analysis

Kyoto protocol (forests as carbon pool)These processes could use indicators as tool for monitoring and reporting of state and progress!

Convention framework for development of indicators:

Sustainable Forest Management (SFM) hierarchy:

PRINCIPLES (Universal)

CRITERIA (General)

INDICATORS (Adapted to local conditions)

VERIFIERS (Basic observations, comparable, can be threshold

values )

ADJUSTING+VALIDATION

ARE THE GOALS ACHIEVED?

SFM Hierarchy

1.MAINTENANCE AND APPROPRIATE ENHANCEMENT OF FOREST RESOURCES AND THEIR CONTRIBUTION TO GLOBAL CARBON CYCLES: Area, Age structure

2.MAINTENANCE OF FOREST ECOSYSTEM HEALTH AND VITALITY : Burned area, Storm damage

3.MAINTENANCE AND ENCOURAGEMENT OF PRODUCTIVE FUNCTIONS OF FORESTS (WOOD AND NON-WOOD): Balance Growth - Removals

4.MAINTENANCE, CONSERVATION AND APPROPRIATE ENHANCEMENT OF BIOLOGICAL DIVERSITY IN FOREST ECOSYSTEMS (Natural forest types)

5.MAINTENANCE AND APPROPRIATE ENHANCEMENT OF PROTECTIVE FUNCTIONS IN FOREST MANAGEMENT (NOTABLY SOIL AND WATER)

6.MAINTENANCE OF OTHER SOCIO-ECONOMIC FUNCTIONS AND CONDITIONS

Helsinki process (MCPFE) criteria:

NATIONAL SCALE  

Structural factors Indicators

Total area of forests Total area (ha)Area in relation to total land area (%)

Afforestation (yearly rate)  

Deforestation (yearly rate)  

Natural regeneration (by 10 years)  

Area of ‘ancient’ woodland Percentage of total area

Compositional factors  

Fire/lightning Number, size and area (% of forest) and age of forest affected

Storms Average annual area of damage

Silvicultural regimes Clearcuts (number and area)Age class frequency in relation to felling area

Agriculture/grazing/browsing Area transformation from agriculture to forestry and vice versa

areas where application of RS data is possible:

BEAR biodiversity indicators

LANDSCAPE SCALE  

Distribution of tree species in different age classes

All species in 20yr age classes up to 250+ years

Representativity of forest biodiversity types Area and percentage of the biodiv. forest types

Old growth forest guild habitat connectivity Spatial pattern of habitat type

Declining trees forest guild habitat connectivity

Spatial pattern of habitat type

Recently disturbed forest guild habitat connectivity

E.g. for boreal forest: area of ground with trees that was burned

Patch size distribution Mean value and st.dev. of patch size

Reasons for stand renewal, abiotic FireWind

STAND SCALE  

Large trees Basal area and/or density

Size of stand In Ha

Shape of stand Area and perimeter (+more advanced?)

BEAR biodiversity indicators, landscape and stand scale

-  Flows of matter, energy, information (across landscapes, soil-vegetation-air)

-  Importance of spatial structure and terrain

-  Disturbance – regeneration (shifting mosaic in natural systems)

- Holistic approach – analysis at “landscape level” – the landscape as a system, hierarchical, multifunctional approach

- Core areas – ecotones

LE concepts

-Island biogeography: species/area-curves

--     Later: Metapopulation ecology

-‘Ancillary’ assumptions:

-Richness of biotope types = richness of habitats

-Interspersion promotes co-habitation of species and movement of indivduals

Core concepts from Landscape Ecology :

PATCH

CLASSCLASS

LANDSCAPEMATRIX

CORRIDOR

STEPPINGSTONES

Landscape concepts

Example 1 : Patterns of forest in the landscape

Shape e.g. edge/area measures

Number of patches, distance measures

Natural Managed

Connected Fragmented

More - less DIVERSE (area presence, distribution measures)

More - less INTERSPERSED (edge length, neighborhood-juxtaposition measures)

Example 2 : Patterns of patches in the forest

Examples of spatial metrics :

Spatial information type

Describing.. Output units

Area Land cover classes or patches m2 , ha, km2, %

Count Objects, patches (richness of) Number

Shape Structure: from patches to landscapes

Any (m-1, FD normally unit-less)

Position, distance Relative placement of patches m, km

Topology Context – connectivity, relative edge type proportions (weighted edge indices)

Unit-less number

less

more

AD

VA

NC

ED

”Information Hierarchy” of Spatial Metrics

Concept “BASIS”Widely accepted as

facts/possible

“POTENTIALS”Under investigation/

discussion

“LIMITS”Not accepted/at the moment not seen as

possible

Land cover Mapping land cover types Mapping habitat types Mapping species presence using EO

Species/ area curves

Species/area relationships exist

Mathematical formulation of S/A relations

Predicting presence/absence of a single species in specific habitat(?)

Landscape structure

Influence of landscape structure on taxonomic diversity

Structural diversity as surrogate for taxonomic diversity, causal links between measures of (abiotic) landscape diversity and taxonomic diversity

Prediction of single species presence solely from landscape diversity information

Landscape Metrics

Calculation of landscape metrics

Meaning of landscape metrics

Relating landscape metric values to abundance of a certain species or directly to taxonomic diversity

What is possible with Landscape Ecology?

What is possible with Landscape Ecology2?

Concept “BASIS”Widely accepted as

facts/possible

“POTENTIALS”Under investigation/

discussion

“LIMITS”Not accepted/at the moment not seen as

possible

Scale Influence of measurement scale on mapping accuracy, metrics values etc.Also on spatial perception by individual animals

Mathematical (spatial statistics) processes influencing spatial metrics, ecological scaling mechanisms governing results from measurement of (local) extinctions dispersal of animal and plants (sampling issues)

‘Grand unifying theory’ of scaling behaviour, reliable prediction of metrics values between imagery at very different scales (?)

Patch-Corridor-Matrix (PCM) model

PCM model can be applied in agricultural landscapes

Applicability of PCM model inside forests

Delineation of functional ‘habitat patches’ in forests (only/purely) from EO data

Corridors Definitions and mapping of corridors in open/high contrast lands

Roles of corridors in landscapes (for specific species), managing for biodiversity by creating corridors

Measuring influence of corridors on taxonomic diversity in landscapes

Who needs forest information ?

* International organisations, NGO’s and environmental organisations

* National ministries

* Research and academic institutes

* Forest Industry

* Forest owners

Forest processes, spatio-temporally

Function, type and level of information

Variable / data type

Forest protection

Stand Forest area (actual/potential ratio)Species CompositionStructure (horizontal, vertical)

Site SoilVegetation typesTopography (elevation, aspect, slope)Climate

Stability Forest condition, Quality, health

Management Value of protected infrastructureWater resourcesObjectives

Forest management information needs 1

Forest management information needs 2

Ecosystem / environment

Variable / data type

Carbon Cycle Woody and herb biomassSoil organic matterClimate

Biodiversity – Ecosystem

Vegetation typeVegetation coverPattern of vegetationNaturalness; management history, age, exotic species Management objectivesForest condition (rate of change)

Biodiversity - Species Species composition (including rare species)Species richness (indicator species)Pattern (corridors / networks)Threats to sp. diversity; human disturbance, pollutant deposition, exotic species

Sustainability Management objectives / history / planning and Land use change

Similarities RS – Landscape Ecology approaches:

* Different processes at different levels;

different scales of observation are relevant

* Integrated (holistic) view

* Pattern does matter(!) – studies of vegetation patterns

* Search for Self-similarity, as reflected in truly fractal patterns

* Minimum mapping unit: Grain = Pixel

* Analysis of scaling effects

* Dealing with spatial heterogeneity..

Similarities RS - LE

What can RS do for forest ecology?

Process steps:

Derived information:

Atmospheric correction, geometric correction, illumination correction

Segmentation / vectorisation / on-screen-digitisation, Land Cover classification

Applying criteriacriteria, using knowledge

Extent of rapid / disastrous processes, such as active fires, clear-cutting, oil spills etc.

Change detection, (based on spectral characteristics)

Area statistics, Spatial metrics, (input to) GIS analysis

Habitat suitability, change sensitivity

Adding value, refinement and compression of informationinformation

Data types: “raw

images”

“orthophotos” etc., rectified, geo-referenced imagery

Land cover maps

Image acquisition

Landscape type maps, habitat type maps, “diversity maps”

From RS to landscape monitoring and valuation

Aerial photo with shape file outlineDominant vegetation type assigned to each polygon

Vectorise/digitise

How to get to land cover maps 1

Landsat TM bands 3,4,5

Forest/non-forest mask

classify raster images

How to get to land cover maps 2

The test case:

One land cover type, the rest “background”

Fragmentation the issue - edge, shape, patch number

[3] 4

1 SqPP

A*

[2] )*(

PPUn

m

1 pixels) ofnumber (total*pixels)forest of(number

pixels ecover typother andforest between runs ofnumber 10* M

Selected spatial metrics for measuring fragmentation

”Moving Windows” Approach

Map 1: Window (user choice): Map 2:

Grain = pixel size = 30m Size (extent) = 9 pixels = 270 m Grain = pixel size = 90 m

Extent = 30*30 pix = 900*900 m Step = 3 pixels = 90 m Extent = 8*8 pixels = 720*720 m

As implemented with calculation of Fragstats-derived and other spatial metrics for “sub-landscapes”

INPUT: “cover type” map(1) OUTPUT: metrics/index value map(2)

DeterminesApplied to

equals

1 2 3 4 5

Calculate

(e.g.)

Patch type

Richness

Maps of spatial metrics, from application of “moving windows”

Total (forest)area

Core Area (TCAI) Diversity (SHDI)

Edge Density

Base = GIS layer of physiological type from regional forest mapping:

Values of spatial metrics:

LOW HIGH

Spatial metrics maps from regional forest map

WiFS, pixel size 200 m TM, pixel size 25 m

50 km

Detected forest cover 54.9%Detected forest cover 44.9%

Results, satellite images, land cover classification

Forest maps from satellite at different resolutions

Displaying landscape metrics

CORINE land cover

WiFS based

FMERS project

Large area maps...

CORINE land cover reclassified to FMERS nomenclature (6 forest classes)

LOW HIGH

Shannon – Simpson diveristy indices

Matheron ’fragmentation’ index

Maps of metrics

Summarization over gridcells

Observation per species

Landscape metrics calculated for

relevant cells, where species are observed

RED=low no. of species BRIGHT GREEN= high no. of species

Umbria, faunal observations

Combination with RS based mapsPresence/non-presence in grid net

CORINE (100m pixels) FMERS - WiFS (200m pixels)

Umbria, mid-Italy, N and E of Assisi, the selected two 2nd order catchments are part of the Tevero (Tiber) catchment (5th order).

Watershed-polygon-statistics example:

Watershed mapping 1

Statistics from 2nd order watersheds

Region 2nd1042.shp 10458 pixels includedBands D:\Geodata\fmers_forestmap\FMERS_Central_other.imgAREA/km2 418,32 %

non-classified 0 16 0,15Coniferous 1 1195 11,43Broadleaved Decid. 2 1040 9,94Broadl. evergreen 3 9 0,09Mixed 4 899 8,60OWL Coniferous 5 695 6,65OWL Broadleaved 6 1646 15,74Other Land 7 4958 47,41Water 8 0 0,00Cloud/Snow 9 0 0,00

Region 2nd1014.shp 16821 pixels includedBands D:\Geodata\fmers_forestmap\FMERS_Central_other.imgAREA/km2 672,84 %

non-classified 0 128 0,76Coniferous 1 766 4,55Broadleaved Decid. 2 2342 13,92Broadl. evergreen 3 34 0,20Mixed 4 1226 7,29OWL Coniferous 5 157 0,93OWL Broadleaved 6 688 4,09Other Land 7 11.480 68,25Water 8 0 0,00Cloud/Snow 9 0 0,00

..calculated indices can be written ’back’ as parameter of WS polygon

Watershed mapping 2

Region 1st5224.shp 1006 pixels includedBands D:\Geodata\fmers_forestmap\FMERS_Central_other.imgAREA/km2 40,24 %

non-classified 0 0 0,00Coniferous 1 19 1,89Broadleaved Decid. 2 3 0,30Broadl. evergreen 3 0 0,00Mixed 4 112 11,13OWL Coniferous 5 0 0,00OWL Broadleaved 6 73 7,26Other Land 7 799 79,42Water 8 0 0,00Cloud/Snow 9 0 0,00

Region 1st5217.shp 3166 pixels includedBands D:\Geodata\fmers_forestmap\FMERS_Central_other.imgAREA/km2 126,64 %

non-classified 0 1 0,03Coniferous 1 523 16,52Broadleaved Decid. 2 338 10,68Broadl. evergreen 3 0 0,00Mixed 4 524 16,55OWL Coniferous 5 224 7,08OWL Broadleaved 6 750 23,69Other Land 7 806 25,46Water 8 0 0,00Cloud/Snow 9 0 0,00

Region 1st5230.shp 869 pixels includedBands D:\Geodata\fmers_forestmap\FMERS_Central_other.imgAREA/km2 34,76 %

non-classified 0 1 0,12Coniferous 1 27 3,11Broadleaved Decid. 2 0 0,00Broadl. evergreen 3 0 0,00Mixed 4 9 1,04OWL Coniferous 5 16 1,84OWL Broadleaved 6 21 2,42Other Land 7 795 91,48Water 8 0 0,00Cloud/Snow 9 0 0,00

http://www.europa.eu.int/comm/agriculture/publi/landscape/ch4.htm

* Apply the spatial metrics land cover maps derived using more sophisticated methods, e.g. edge preserving smoothing, segmentation and/or neural networks.

* Multiple regression of metrics such as the ones studied here or other parameters describing ecological conditions.

* Verify how indices derived from classifications of aerial photos of the area (preferably ~1 m resolution), relate to satellite data.

* Comparison with CORINE land cover data, taking into account that:

- Coverages are not regularly updated (not to be used for monitoring)- The dataset is originally vector based, some information is lost when converted to raster format, not intended to be used as a pixel based land-cover mask.

Further work..

- (-Infinitely) Many spatial metrics can be calculated from EO-data, but connections with ecological conditions must be established and their use verified.

Conclusions

- The role of Remote Sensing and other Earth Observation techniques concerning forest management is to complement other information sources and inventories done by specialised researchers on the ground.- GIS is an adequate tool for combining information

stored in data-bases, map information and EO-data.

- Remote sensing provides synoptic images at different scales, potentially making it a powerful tool for applications in multi-scale landscape analysis.

- Moving Windows approaches can provide information on landscape sturcture and forest diversity over large areas – illustrating distributions and highlighting ’hot-spots’.

Land Use Planning Decision making

Administration

Spatial Metrics

Thematic Maps

Digital Imagery

Indicators

InventoriesLand/Forest Management

Earth Observation

Monitoring

Trad. Forestry / Ecological - Environmental

Sketch of Terra satellite ©NASA, 2000

Do spatial metrics fit in somewhere?

RS – spatial metrics

* Development of methods for detection of areas threatened or in need of special management techniques/consideration.

& research needs

* Satellites with higher spatial resolution + satellites with multi-spectral sensors – extended spatial and spectral domains.

* Still a need for better understanding of how to relate spatial/textural measures/information from high resolution to medium scale spectral and/or spatial information.

* Watersheds as natural regions for calcultaion and reporting of spatial/structural landscape properties...

Future options