《景观生态学》（英文版） LANDSCAPE ECOLOGY SREM 3011 LECTURE 8

LANDSCAPE ECOLOGY SREM 3011 LECTURE 8 Dr Brendan Mackey Department of Geography The Australian National University

LANDSCAPE ECOLOGY SREM 3011 LECTURE 8 Dr Brendan Mackey Department of Geography The Australian National University

Scale'-key concept for unifying landscape ecology integral component of scientific method of Inquiry 1. Different notions of scale 2. Scale dependent independent biological and environmental attributes and concepts 3. Ecological hierarchy theory 4. Implications for spatial landscape ecology models A Unified Theory of Ecology Allen Hoekstra

‘Scale’ - key concept for unifying landscape ecology - integral component of scientific method of inquiry 1. Different notions of scale 2. Scale dependent /independent biological and environmental attributes and concepts 3. Ecological hierarchy theory 4. Implications for spatial landscape ecology models ‘A Unified Theory of Ecology’ Allen & Hoekstra

Different notions or dimensions of scale: 1. Cartographic scale 2. Space time 3. Grain 4. Extent 5. Resolution 6. Scale-dependency 7. Landscape texture 1:1000000,1:20000000 1:5000 1:10000 Large scale-big slow Small scale-small+ fast

Different notions or dimensions of scale: 1. Cartographic scale 2. Space & time 3. Grain 4. Extent 5. Resolution 6. Scale-dependency 7. Landscape texture 1 : 1 000 000, 1 : 20 000 000 1 : 5 000 1 : 10 000 Large scale - big + slow Small scale - small + fast

ypical organization of a gis database in a land management organization; the primary division is on the basis of scale, subsequent divisions are into theme dures, such as ensuring a common boundaries where appropriate mean tha and location Numerous scale-dependent applications and administrative pro scale must be the primary organizational basis Spatial dataset separation of scales 1:1.000,000 100,000 1:25.000 separation by theme

Typical organization of a GIS database in a land management organization; the primary division is on the basis of scale, subsequent divisions are into theme and location. Numerous scale-dependent applications and administrative proce￾dures, such as ensuring a common boundaries where appropriate, mean that scale must be the primary organizational basis

Scale has two dimensions: Space and 2. Time Therefore, large scale phenomena are generally spatially big and change slowly Small scale phenomena are generally spatially little and change quickly Large scale= low frequency Small scale= high frequency eg Fire regime experienced by a forest (rate of return)

Scale has two dimensions: 1. Space and 2. Time Therefore, large scale phenomena are generally spatially big and change slowly Small scale phenomena are generally spatially little and change quickly • Large scale = low frequency Small scale = high frequency eg. Fire regime experienced by a forest (‘rate of return’)

Grain smallest unit of observation and measurement smallest and most ephemeral entities that can be found in the data Eg. Microscope Unicellular algae 1m x 1m pit Soil survey 20m x 20m plot Vegetation survey 1 ha plot Vertebrate survey Grain=“ Filter3 human primary senses minimum unit of measurement. size of measuring ' rod

Grain = smallest unit of observation and measurement = smallest and most ephemeral entities that can be found in the data Eg. Microscope Unicellular algae 1m x 1m pit Soil survey 20m x 20m plot Vegetation survey 1 ha plot Vertebrate survey • Grain = ‘Filter’ - human primary senses - minimum unit of measurement; size of measuring ‘rod’

A simulated inventory of forest cover undertaken at several scales. A: Hypothetical tree locations and crown extents; crowns are considered to be impermeable. B to F: the results of an inventory carried out with grain-size determined by a disc of diameter equal to the vertical bar at the bottom right of each window. For each location on the grid, the percent of the disc area within crowns is calculated. The cell is classified as forest if this mean exceeds 40%. As grain size increased, the inventory results change substantially At grain size F, the inventory is clearly missing features which one feels are important, but at grain size B, the concept of iforest'is not being g the entire area would be determined as forest, despite the fact that only 50%otthswvas ddressed; small intervals between trees are clearly part of the forest. If grain-size increased beyond area is occupied by tree crowns B E冷

A simulated inventory of forest cover undertaken at several scales. A:Hypothetical tree locations and crown extents; crowns are considered to be impermeable. B to F: the results of an inventory carried out with grain-size determined by a disc of diameter equal to the vertical bar at the bottom right of each window. For each location on the grid, the percent of the disc area within crowns is calculated. The cell is classified as ‘forest’ if this mean exceeds 40%. As grain size increased, the inventory results change substantially.At grain size F, the inventory is clearly missing features which one feels are important, but at grain size B, the concept of ‘forest’ is not being addressed; small intervals between trees are clearly part of the forest. If grain-size was increased beyond F, the entire area would be determined as forest, despite the fact that only 50% of the area is occupied by tree crowns. A B C D E F

Grain- sets lower limit to what we observe Extent-sets larger limits to which observations or study apply the extent of a study has to be bigger than the size of the object/phenomena boundary definition influences spatial analyses fine grain often(but not always)means narrow extent Resolution spatial unit to which data are geo referenced raster data(eg. DEM): Resolution= Grid spacing remote-sensing Grain Resolution Pixel Size Grid spacing

Grain - sets lower limit to what we observe Extent - sets larger limits to which observations or study apply the extent of a study has to be bigger than the size of the object/phenomena boundary definition influences spatial analyses fine grain often (but not always) means narrow extent Resolution = spatial unit to which data are geo￾referenced raster data (eg. DEM): Resolution = Grid spacing remote-sensing: Grain = Resolution = Pixel Size = Grid spacing

Extent, grain and resolution. Possible alternative measures of scale. A, the extent is the interval of observation, which determines the largest scale observable; B, the grain is the fineness of obser vation, which determines the smallest scale observable. C, the resolution is often equated with the grain, but clearly, this can be misleading as the resolution can be finer than the grain Extent 3 Grain "γ Resolution

Extent, grain and resolution. Possible alternative measures of scale. A, the extent is the interval of observation, which determines the largest scale observable; B, the grain is the fineness of obser￾vation, which determines the smallest scale observable. C, the resolution is often equated with the grain, but clearly, this can be misleading as the resolution can be finer than the grain. Extent Grain Resolution

Resolution A Grid spacing Larger B Smaller

Resolution Grid spacing Larger Smaller