Intro

Intro

Intro NordLaM Nordic Workshop: Deriving Indicators from Earth Observation Data - Limitations and Potential for Landscape Monitoring, 22nd - 23rd October, Drbak, Norway The potential of landscape metrics from Remote Sensing data as indicators in forest environments Niels Chr. Nielsen, M.Sc. [email protected] 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 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 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 Convention framework for development of indicators: 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! SFM Hierarchy Sustainable Forest Management (SFM) hierarchy: PRINCIPLES (Universal)

CRITERIA (General) ARE THE GOALS ACHIEVED? INDICATORS (Adapted to local conditions) ADJUSTING +VALIDATION VERIFIERS (Basic observations, comparable, can be threshold values ) Helsinki process (MCPFE) criteria: 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 BEAR biodiversity indicators areas where application of RS data is possible: 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 Storms Number, size and area (% of forest) and age of forest affected 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 BEAR biodiversity indicators, landscape and stand scale 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 Fire Wind STAND SCALE Large trees Basal area and/or density Size of stand In Ha

Shape of stand Area and perimeter (+more advanced?) Core concepts from Landscape Ecology : - Flows of matter, energy, information (across landscapes, soilvegetation-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 -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 LE concepts Landscape concepts LANDSCAPE MATRIX CORRIDOR

CLASS PATCH STEPPING STONES Example 1 : Patterns of forest in the landscape Natural Managed Shape e.g. edge/area measures Connected Fragmented Number of patches, distance measures Example 2 : Patterns of patches in the forest More - less DIVERSE (area presence, distribution measures) More - less INTERSPERSED (edge length, neighborhood-juxtaposition measures)

Examples of spatial metrics : Information Hierarchy 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 ADVANCED less more What is possible with Landscape Ecology? 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 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 (?) PatchCorridorMatrix

(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, NGOs and environmental organisations * National ministries * Research and academic institutes * Forest Industry * Forest owners Forest processes, spatio-temporally Forest management information needs 1 Function, type and level of information Variable / data type Forest protection Stand Forest area (actual/potential ratio) Species Composition Structure (horizontal, vertical) Site

Soil Vegetation types Topography (elevation, aspect, slope) Climate Stability Forest condition, Quality, health Management Value of protected infrastructure Water resources Objectives Forest management information needs 2 Ecosystem / environment Variable / data type Carbon Cycle Woody and herb biomass Soil organic matter Climate

Biodiversity Ecosystem Vegetation type Vegetation cover Pattern of vegetation Naturalness; management history, age, exotic species Management objectives Forest 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 - LE 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 * Dealing with spatial heterogeneity.. * Search for Self-similarity, as reflected in truly fractal patterns * Analysis of scaling effects * Minimum mapping unit: Grain = Pixel What can RS do for forest ecology? From RS to landscape monitoring and valuation Adding value, refinement and compression of information Proces s steps:Image acquisition Data types: Atmospheric correction, geometric correction, illumination correction

raw image s Segmentation / vectorisation / on-screendigitisation, Land Cover classification orthophot os etc., rectified, georeferenced imagery Derived information : Extent of rapid / disastrous Change detection, (based on spectral characteristics) processes, such as active fires, clear-cutting, oil spills etc. Land cover

maps Applying criteria, criteria using knowledge Landscape type maps, habitat type maps, diversity maps Area statistics, Spatial metrics, (input to) GIS analysis Habitat suitability, change sensitivity How to get to land cover maps 1 Vectorise/digitise Aerial photo with shape file outline

Dominant vegetation type assigned to each polygon How to get to land cover maps 2 classify raster images Landsat TM bands 3,4,5 Forest/nonforest mask Selected spatial metrics for measuring fragmentation The test case: One land cover type, the rest background Fragmentation the issue - edge, shape, patch number M 10* number of runs between forest and other cover type pixels (number of forest pixels) * (total number of pixels) PPU m (n * ) SqP 1

4* A P 1 [2] [3] Moving Windows Approach 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) Calculate (e.g.) Patch type Richness 1 2 3 4 5 Map 1: Applied to Grain = pixel size = 30m

Extent = 30*30 pix = 900*900 m Window (user choice): Size (extent) = 9 pixels = 270 m Step = 3 pixels = 90 m Determines ls equa Map 2: Grain = pixel size = 90 m Extent = 8*8 pixels = 720*720 m Spatial metrics maps from regional forest map Maps of spatial metrics, from application of moving windows Base = GIS layer of physiological type from regional forest mapping: Edge Density Total (forest)area Values of spatial metrics:

Core Area (TCAI) LOW HIGH Diversity (SHDI) Forest maps from satellite at different resolutions Results, satellite images, land cover classification TM, pixel size 25 m WiFS, pixel size 200 m Detected forest cover 44.9% Detected forest cover 54.9% 50 km

Displaying landscape metrics Large area maps... WiFS based FMERS project CORINE land cover CORINE land cover reclassified to FMERS nomenclature (6 forest classes) Maps of metrics Matheron fragmentation index LOW HIGH Shannon Simpson diveristy indices Umbria, faunal observations RED=low no. of species GREEN= high no. of species

Observation per species BRIGHT Summarization over gridcells Landscape metrics calculated for relevant cells, where species are observed Combination with RS based maps Presence/non-presence in grid net CORINE (100m pixels) FMERS - WiFS (200m pixels) Watershed-polygon-statistics example: Umbria, mid-Italy, N and E of Assisi, the selected two 2nd order catchments are part of the Tevero (Tiber) catchment (5th order).

Watershed mapping 1 Statistics from 2nd order watersheds Region Bands AREA/km2 non-classified Coniferous Broadleaved Decid. Broadl. evergreen Mixed OWL Coniferous OWL Broadleaved Other Land Water Cloud/Snow 0 1 2 3 4 5 6 7 8 9 2nd1014.shp

16821 pixels included D:\Geodata\fmers_forestmap\FMERS_Central_other.img 672,84 % 128 0,76 766 4,55 2342 13,92 34 0,20 1226 7,29 157 0,93 688 4,09 11.480 68,25 0 0,00 0 0,00 0 1 2

3 4 5 6 7 8 9 2nd1042.shp 10458 pixels included D:\Geodata\fmers_forestmap\FMERS_Central_other.img 418,32 % 16 0,15 1195 11,43 1040 9,94 9 0,09 899 8,60 695 6,65 1646 15,74 4958

47,41 0 0,00 0 0,00 Region Bands AREA/km2 non-classified Coniferous Broadleaved Decid. Broadl. evergreen Mixed OWL Coniferous OWL Broadleaved Other Land Water Cloud/Snow ..calculated indices can be written back as parameter of WS polygon Watershed mapping 2 Region Bands AREA/km2

non-classified Coniferous Broadleaved Decid. Broadl. evergreen Mixed OWL Coniferous OWL Broadleaved Other Land Water Cloud/Snow 0 1 2 3 4 5 6 7 8 9 1st5224.shp 1006 pixels included D:\Geodata\fmers_forestmap\FMERS_Central_other.img 40,24 % 0

0,00 19 1,89 3 0,30 0 0,00 112 11,13 0 0,00 73 7,26 799 79,42 0 0,00 0 0,00 Region Bands AREA/km2 non-classified Coniferous Broadleaved Decid. Broadl. evergreen Mixed

OWL Coniferous OWL Broadleaved Other Land Water Cloud/Snow 0 1 2 3 4 5 6 7 8 9 1st5230.shp 869 pixels included D:\Geodata\fmers_forestmap\FMERS_Central_other.img 34,76 % 1 0,12 27 3,11 0 0,00

0 0,00 9 1,04 16 1,84 21 2,42 795 91,48 0 0,00 0 0,00 Region Bands AREA/km2 non-classified Coniferous Broadleaved Decid. Broadl. evergreen Mixed OWL Coniferous OWL Broadleaved Other Land Water Cloud/Snow

0 1 2 3 4 5 6 7 8 9 1st5217.shp 3166 pixels included D:\Geodata\fmers_forestmap\FMERS_Central_other.img 126,64 % 1 0,03 523 16,52 338 10,68 0 0,00 524 16,55 224

7,08 750 23,69 806 25,46 0 0,00 0 0,00 http://www.europa.eu.int/comm/agriculture/publi/landscape/ch4.htm Further work.. * 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.

Conclusions - Remote sensing provides synoptic images at different scales, potentially making it a powerful tool for applications in multi-scale landscape analysis. - 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. - Moving Windows approaches can provide information on landscape sturcture and forest diversity over large areas illustrating distributions and highlighting hotspots. - (-Infinitely) Many spatial metrics can be calculated from EO-data, but connections with ecological conditions must be established and their use verified. RS spatial metrics Do spatial metrics fit in somewhere? Sketch of Terra satellite NASA, 2000 Digital Imagery

Thematic Maps Land Use Planning Decision making Administration Spatial Metrics Indicators Earth Observation Monitoring Inventories Trad. Forestry / Ecological Environmental Land/Forest Management & research Future options

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

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