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The potential of landscape metrics from Remote Sensing data as indicators in forest environments Niels Chr. Nielsen, M.Sc.

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Presentation on theme: "The potential of landscape metrics from Remote Sensing data as indicators in forest environments Niels Chr. Nielsen, M.Sc."— Presentation transcript:


2 The potential of landscape metrics from Remote Sensing data as indicators in forest environments Niels Chr. Nielsen, M.Sc. 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 - 23 rd October, Drøbak, Norway

3 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

4 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:

5 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


7 NATIONAL SCALE Structural factorsIndicators Total area of forestsTotal area (ha) Area in relation to total land area (%) Afforestation (yearly rate) Deforestation (yearly rate) Natural regeneration (by 10 years) Area of ‘ancient’ woodlandPercentage of total area Compositional factors Fire/lightningNumber, size and area (% of forest) and age of forest affected StormsAverage annual area of damage Silvicultural regimesClearcuts (number and area) Age class frequency in relation to felling area Agriculture/grazing/browsingArea transformation from agriculture to forestry and vice versa areas where application of RS data is possible: BEAR biodiversity indicators

8 LANDSCAPE SCALE Distribution of tree species in different age classes All species in 20yr age classes up to 250+ years Representativity of forest biodiversity typesArea and percentage of the biodiv. forest types Old growth forest guild habitat connectivitySpatial pattern of habitat type Declining trees forest guild habitat connectivitySpatial 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 distributionMean value and of patch size Reasons for stand renewal, abioticFire Wind STAND SCALE Large treesBasal area and/or density Size of standIn Ha Shape of standArea and perimeter (+more advanced?) BEAR biodiversity indicators, landscape and stand scale

9 - Flows of matter, energy, information (across landscapes, soil- vegetation-air) - I mportance of spatial structure and terrain - D isturbance – 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 :


11 Example 1 : Patterns of forest in the landscape Shape e.g. edge/area measures Number of patches, distance measures Natural Managed Connected Fragmented

12 More - less DIVERSE (area presence, distribution measures) More - less INTERSPERSED (edge length, neighborhood-juxtaposition measures) Example 2 : Patterns of patches in the forest

13 Examples of spatial metrics :

14 Spatial information type Describing..Output units AreaLand cover classes or patchesm 2, ha, km 2, % CountObjects, patches (richness of)Number ShapeStructure: from patches to landscapes Any (m -1, FD normally unit- less) Position, distanceRelative placement of patchesm, km TopologyContext – connectivity, relative edge type proportions (weighted edge indices) Unit-less number less more ADVANCED ”Information Hierarchy” of Spatial Metrics

15 Concept“BASIS” Widely accepted as facts/possible “POTENTIALS” Under investigation/ discussion “LIMITS” Not accepted/at the moment not seen as possible Land coverMapping land cover typesMapping habitat typesMapping 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?

16 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

17 Who needs forest information ? * International organisations, NGO’s and environmental organisations * National ministries * Research and academic institutes * Forest Industry * Forest owners

18 Forest processes, spatio-temporally

19 Function, type and level of information Variable / data type Forest protection StandForest area (actual/potential ratio) Species Composition Structure (horizontal, vertical) SiteSoil Vegetation types Topography (elevation, aspect, slope) Climate StabilityForest condition, Quality, health ManagementValue of protected infrastructure Water resources Objectives Forest management information needs 1

20 Forest management information needs 2 Ecosystem / environment Variable / data type Carbon CycleWoody 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 - SpeciesSpecies composition (including rare species) Species richness (indicator species) Pattern (corridors / networks) Threats to sp. diversity; human disturbance, pollutant deposition, exotic species SustainabilityManagement objectives / history / planning and Land use change

21 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

22 What can RS do for forest ecology?

23 Process steps: Derived information: Atmospheric correction, geometric correction, illumination correction Segmentation / vectorisation / on-screen- digitisation, Land Cover classification Applying criteria criteria, 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 information Adding value, refinement and compression of information 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

24 Aerial photo with shape file outline Dominant vegetation type assigned to each polygon Vectorise/digitise How to get to land cover maps 1

25 Landsat TM bands 3,4,5 Forest/non- forest mask classify raster images How to get to land cover maps 2

26 The test case: One land cover type, the rest “background” Fragmentation the issue - edge, shape, patch number Selected spatial metrics for measuring fragmentation

27 ”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 mStep = 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) Determines Applied to equals 1 2 3 4 5 Calculate (e.g.) Patch type Richness

28 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

29 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

30 Displaying landscape metrics

31 CORINE land cover WiFS based FMERS project Large area maps... CORINE land cover reclassified to FMERS nomenclature (6 forest classes)

32 LOWHIGH Shannon – Simpson diveristy indices Matheron ’fragmentation’ index Maps of metrics

33 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

34 Combination with RS based maps Presence/non-presence in grid net CORINE (100m pixels)FMERS - WiFS (200m pixels)

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

36 Watershed mapping 1 Statistics from 2 nd order watersheds..calculated indices can be written ’back’ as parameter of WS polygon

37 Watershed mapping 2

38 * 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..

39 - (-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’.

40 Land Use Planning Decision making Administration Spatial Metrics Thematic Maps Digital Imagery Indicators Inventories Land/Forest Management Earth Observation Monitoring Trad. Forestry / Ecological - Environmental Sketch of Terra satellite ©NASA, 2000 Do spatial metrics fit in somewhere? RS – spatial metrics

41 * 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

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