Landscape Metrics. Prof. Dr. Adrienne Grêt-Regamey Sibyl Brunner Ana Stritih

Transcription

1 Landscape Metrics Prof. Dr. Adrienne Grêt-Regamey Sibyl Brunner Ana Stritih

2 Landscape structure Block 2: Landscape assessment (1) Descriptive analysis (2) Comparison of landscapes (3) Comparison of alternatives (4) Monitoring 2 53

3 Overview Clearly, the objective of applying landscape metrics goes beyond describing and measuring patterns: its aim is to explain and understand the processes that occur. (Haines-Young, 1999) From last time: (1) Data classification Arable land Intensive grassland Forest Matrix: Extensive grassland (2) Scale 100m x 100m (3) Patch definition => (4) Selection and (5) Interpretation of landscape metrics 3 53

4 Landscape metrics 4 53 Lang und Blaschke, 2007

5 Area metrics Example: Patch area = 9 raster cells 25m x 25m = m2 Area = Raster_Cells Area = Area OAB +Area OBC + Area OCD + Area ODE - Area OAE Lang and Blaschke, Patch Size (PS) = Patch area

6 Meaning Habitat, minimal size Lynx, single individual: km 2 Lynx, viable population: km

7 Area metrics Short exercise: Calculate Patch Size for the selected patches. 100m x 100m 7 53

8 Area metrics Short exercise: Calculate Patch Size for the selected patches. Green Patch: 7 x 100m x 100m = m 2 Red Patch: 5 x 100m x 100m = m 2 100m x 100m 8 53

9 Edge metrics Lang und Blaschke, Total Edge = Total length of edges Edge Density = Edge length per landscape area

10 Meaning Connectivity Complexity Ground-nesting birds: skylark, lapwing, gray partridge Michel and Walz,

11 Caution Human impacts: Road construction "good" versus "bad" Digitalized data: Artificial identification of boundary lines "real" versus "artificial" Quality of the edge lines! 11 53

12 Edge metrics Short exercise: Calculate Total Edge and Edge Density for the selected patches. Which metric is more meaningful? 100m x 100m 12 53

13 Edge metrics Short exercise: Calculate Total Edge and Edge Density for the selected patches. Which metric is more meaningful? Yellow patch: Total Edge: 18 x 100m = 1800m Edge Density: 1800m/20ha = 90m/ha = 0.009m -1 Red patch: Total Edge: 22 x 100m = 2200m Edge Density: 2200m/11ha = 200m/ha = 0.02m m x 100m 13 53

14 Core area metrics Core Area (CA) = Core area of a patch Lang and Blaschke,

15 Core area metrics Lang und Blaschke, Core Area Index (CAI) = Percentage of the patch that is comprised of core area

16 Core area metrics (landscape) CY = NP N CAI=0 NCA NP = Number of patches N CAI=0 = Cases without core areas NCA = Number of core areas Lang und Blaschke, Cority (CY) = Fragmentation with respect to a core area distance

17 Meaning Effective habitat area Erosion risk Habitat edge area Red kite 17 53

18 Core area metrics Short exercise: Calculate the landscape-level Core Area Index and the Cority for the yellow patches and for a core area distance of 100m. What values can Cority have? 100m x 100m 18 53

19 Core area metrics Short exercise: Calculate the landscape-level Core Area Index and the Cority for the yellow patches and for a core area distance of 100m. What values can Cority have? 1 4 Patch 1 CAI = 6ha/20ha = 0.3 Patch 2 CAI = 0 Patch 3 CAI = 1ha/9ha = m x 100m Patch 4 CAI = 1ha/18ha = 0.06 Cority = (4 1)/3 =

20 Shape metrics Area/Perimeter or Perimeter/Area? Lang und Blaschke, Area-Perimeter Ratio = Ratio of area to perimeter

21 Shape metrics Short exercise: Calculate the Area-Perimeter-Ratio for the two highlighted patches. What jumps out, and how can you address it? 100m x 100m 21 53

22 Shape metrics Short exercise: Calculate the Area-Perimeter-Ratio for the two highlighted patches. What jumps out, and how can you address it? Patch 1 Area/Perimeter = m 2 /800m = 50m Patch 2 Area/Perimeter = m 2 /1200m = 75m 100m x 100m 22 53

23 Shape metrics p = perimeter, a = area Shape Index = Deviation from a circular shape 23 53

24 Application Forest planning: Minimization of shape index Korosue et al., 2014 Habitat fragmentation 24 53

25 Shape metrics Fractal dimension (D) = Irregularity of a patch

26 Shape metrics p = perimeter, a = area Lang and Blaschke, Ex. Fractal dimension (D) = Irregularity of a patch

27 Compactness metrics Radius of gyration = largest circle around the patch Lang und Blaschke,

28 Meaning Habitat suitability Landscape scenery 28 53

29 Mean patch metrics Mean Patch Size (MPS) = average patch size MPS = j=1 n n i a ij Patch Size Standard Deviation (PSSD) = spread of the patch size PSSD = n j=1 a ij n i j=1 n n i a ij 2 n = number of patches, a = area Patch Density (PD) = number of patches per ha

30 Application Bielsa et al

31 Mean patch metrics Short exercise: Calculate the MPS, PSSD and PD for the class forest (green patches). Which metric gives the most information? 100m x 100m 31 53

32 Mean patch metrics Short exercise: Calculate the MPS, PSSD and PD for the class forest (green patches). Which metric gives the most information? 100m x 100m Mean Patch Size (MPS): (7ha + 8ha + 4ha + 13ha)/4 = 8ha Patch Size Standard Deviation (PSSD): Variance: ((7ha 8ha) 2 + (8ha 8ha) 2 + (4ha 8ha) 2 + (13ha 8ha) 2 ))/4 = (( ) ha 2 )/4 = 10.5 ha 2 Standard deviation: 3.24ha Patch Density (PD): 4 patches in 288ha = Patches/ha 32 53

33 Mean patch metrics Problem: PSSD large absolute differences, even when relative similar variance PSSD = 2 ha PSSD = 2 m 2 100m x 100m 1m x 1m Patch Size Coefficient of Variation (PSCV) = standardized spread of the patch sizes = (PSSD/MPS) * PSCV = 50% PSCV = 50%

34 Proximity metrics Euclidean Distance (d) = shortest distance between two patches Nearest-Neighbor-Distance = minimal distance to target patch in the same class Mean-Nearest-Neighbor-Distance = mean distance to target patch in the same class Question: Why are these metrics problematic? Lang and Blaschke,

35 Proximity metrics Proximity buffer Various possibilities for the combination of Distance Area Proximity index (PX) = Patch isolation and fragmentation of the patch type 35 53

36 Area: Focal patch Area: both Area: Target patch Lang and Blaschke, 2007 Proximity metrics Distance: nearest neighbor Distance: all patches in PB Variante D: PX 94 = A f d + i=1 n At d At = Area target patch d = Distance Af = Area focal patch

37 Meaning Habitat connectivity Isolation Hamster: Radius of activity 195m Lizard: Maximum dispersion distance 300m

38 Proximity metrics Short exercise: Calculate the Proximity Index PX fg for the two indicated patches (Assumption: all visible patches are within the Proximity Buffer). 100m x 100m 38 53

39 Proximity metrics Short exercise: Calculate the Proximity Index PX fg for the two indicated patches (Assumption: all visible patches are within the Proximity Buffer). Yellow Patch: 4 x m 2 /400m+ 16 x m 2 /300m = 633m 100m x 100m Red Patch: 12 x m 2 /400m + 5 x m 2 /300m + 11 x m 2 /800m + 10 x m 2 /200m = 1104m 39 53

40 Diversity metrics s = number of classes p i = proportional coverage of class "i" H = s i=1 (p i ) ln(p i ) Shannon diversity (H) = distribution of patch types EVEN = H ln(s) DOM = ln s H Evenness (EVEN) = standardized diversity, even distribution of area between patch types Dominance (DOM) = deviation from maximum diversity 40 53

41 Meaning Biodiversity Landscape scenery 41 53

42 Diversity metrics Short exercise: Calculate the Shannon-Diversity-Index, Evenness and Dominance for the exercise landscape. 100m x 100m 42 53

43 Diversity metrics Short exercise: Calculate the Shannon-Diversity-Index, Evenness and Dominance for the exercise landscape. Proportional coverage green: 52ha/288ha = 0.18 Proportional coverage yellow: 29ha/288ha = 0.10 Proportional coverage red: 46ha/288ha = 0.16 Proportional coverage gray: 0.56 H = - (0.18*ln(0.18) *ln(0.10) *ln(0.16) *ln(0.56)) = 1.15 EVEN = 1.15/ln(4) = 1.15/1.39 = 0.83 DOM = ln(4) 1.15 = =

44 Lang and Blaschke, 2007 Diversity metrics % Forest, 25% Water, 69% Agriculture 6% Agriculture, 25% Forest, 69% Water

45 Diversity Limmattal Based on "Arealstatistik" (100 x 100 m Raster), 4 Categories Shannon Diversity: 1.12 Shannon Evenness: 0.81 Based on "Arealstatistik" (100 x 100 m Raster), 72 Categories Shannon Diversity: 2.98 Shannon Evenness:

46 Fragmentation metrics Ftotal = total area n = number of patches Fi = area of a patch Jaeger, Lang and Blaschke, 2007 Effective Mesh Size (m eff ) = Size of remaining residual areas; expresses the probability that two points chosen randomly in a region are connected.

47 Urban sprawl metrics Urban sprawl (DE: "Zersiedelung") = Extent of buildings in the landscape and their dispersion New metrics: Schwick et al., 2010

48 Settlement units per km2 Urban sprawl metrics DIS = Dispersion, TS = Total settlement (Siedlungsfläche = settlement area; Grösse der Landschaft = size of the landscape) Jaeger et al., 2008 Urban Penetration (UP) = Penetration of urban area in the landscape 48 53

49 Settlement units per km2 Urban sprawl metrics DIS = Dispersion, TS = Total settlement (Siedlungsfläche = settlement area; Grösse der Landschaft = size of the landscape) Jaeger et al., 2008 Dispersion (DIS) = Scattering / dispersion of the urban area 49 53

50 Urban sprawl Limmattal Dispersion High! > 46 DSE/m 2 (similar to Bern, Lausanne, Lugano ) Area utilization per person Small! < 200 m 2 /person (similar to Bern, Lausanne, Lugano ) Jaeger et al., 2008

51 Significance Habitat loss Barrier effect Fragmentation Mortality Species loss Aesthetics Recreation Emissions 51 53

52 Fragmentation metrics Short exercise: Calculate the Effective Mesh Size for the landscape, which is divided by two roads into three areas; assume that an animal can move around in all natural habitats. What are the maximum and minimum values of the metric? 100m x 100m 52 53

53 Fragmentation metrics Short exercise: Calculate the Effective Mesh Size for the landscape, which is divided by two roads into three areas; assume that an animal can move around in all natural habitats. What are the maximum and minimum values of the metric? Effective Mesh Size: 288ha x ((48/288) 2 + (60/288) 2 + (180/288) 2 ) = 133ha 100m x 100m 53 53

54 Application Landscape indicators The state and evolution of the environment in the landscape are illustrated and evaluated based on selected characteristics Indicator: landscape fragmentation Indicator: urban sprawl BAFU Schweiz, Indikatoren der Umwelt 54 53

55 Learning goals and materials Leaning goals: You have a sense for LSM and can interpret formulae for previously unknown LSM know the strengths and weaknesses of different LSM at various levels can design a LSM for a given problem statement can apply and calculate LSM in exercise landscapes know the significance and applicability of different LSM understand extreme values (max, min) of LSM do not have to learn by heart the exact formula of all LSM, but rather understand which metrics are necessary for certain purposes Slides and background literature will be available for download on the course website... as well as the answers to the short exercises 55 53