FSH Fine-Scale Heterogeneity Index

A key component of forest structure descriptions is the spatial heterogeneity (i.e., tree clumps and gaps), which influences vegetation growth, competition, and succession, disturbance processes, and wildlife habitat. Developing spatial heterogeneity through mechanical and prescribed fire treatments is often a goal of restoration projects and targets for the distribution of individual trees, clumps and gaps are often derived from historical estimates of stand structure.

This fractal dimension index is intended to be used in combination with the percent canopy cover as a measure of fine-scale heterogeneity. Fine-scale heterogeneity in forest structure may interrupt fuel continuity and reduce mortality of overstory trees. Fractal dimension is a measure of the complexity of shapes and ranges from 1, for simple shapes (fewer canopy interruptions), to 2, for complex shapes (more canopy interruptions). Fractal dimension is typically applied to single-part shapes, here we apply it to forest canopy within a 90m x 90m moving window.

The following diagram illustrates how fractal dimension index values correspond with spatial patterns of forest canopy coverage. Green areas denote canopy coverage and brown areas denote low-growing vegetation or bare areas. Areas where the shape of canopy coverage is more complex or patchy thereby have higher fractal area index.

Image courtesy of Jonathan T. Kane, University of Washington.

Data and Resources

Additional Info

Field Value
Last Updated January 17, 2025, 06:42 (UTC)
Created January 17, 2025, 06:42 (UTC)
category /Forest and Shrubland Resilience/Structure
collection_name California Landscape Metrics
creation_method The metric is derived from 3m resolution PhoDAR estimates of spring 2020 canopy height produced by Salo Sciences. Pixels with height greater than 2m were classified as canopy; pixels with height less than or equal to 2m were classified as canopy gaps. Fractal dimension index was calculated within a 90m (900-pixel) moving window using the following expression, applicable to shapes represented by rectilinear pixels (McGarigal and Marks 1995). This data layer currently exists only for the Sierra Nevada region. _2*ln(p/4)/ln(a)_ Where _a_ and _p_ are, respectively, the area and perimeter of forest canopy (height > 2m) within the moving window.
data_units Fractal dimension index, 1 to 2
data_vintage 2020
encoding utf8
file_name SNV_FineScaleHeteroIndex_202006_202209_T2_v5
format GeoTiff
harvest_object_id 2aca8da8-ef63-4409-bc41-0a120ac98bfa
harvest_source_id a2637971-af12-457f-ae4a-831d2202a539
harvest_source_title WIFIRE Commons
maximum_value 2.0
metric_definition_and_relevance A key component of forest structure descriptions is the spatial heterogeneity (i.e., tree clumps and gaps), which influences vegetation growth, competition, and succession, disturbance processes, and wildlife habitat. Developing spatial heterogeneity through mechanical and prescribed fire treatments is often a goal of restoration projects and targets for the distribution of individual trees, clumps and gaps are often derived from historical estimates of stand structure. This fractal dimension index is intended to be used in combination with the percent canopy cover as a measure of fine-scale heterogeneity. Fine-scale heterogeneity in forest structure may interrupt fuel continuity and reduce mortality of overstory trees. Fractal dimension is a measure of the complexity of shapes and ranges from 1, for simple shapes (fewer canopy interruptions), to 2, for complex shapes (more canopy interruptions). Fractal dimension is typically applied to single-part shapes, here we apply it to forest canopy within a 90m x 90m moving window. The following diagram illustrates how fractal dimension index values correspond with spatial patterns of forest canopy coverage. Green areas denote canopy coverage and brown areas denote low-growing vegetation or bare areas. Areas where the shape of canopy coverage is more complex or patchy thereby have higher fractal area index. _Image courtesy of Jonathan T. Kane, University of Washington._
minimum_value 1.0
spatial {"type": "Polygon", "coordinates": [[[-122.2883616476824, 35.05769372687348], [-117.5563290826801, 35.05769372687348], [-117.5563290826801, 42.00332292672577], [-122.2883616476824, 42.00332292672577], [-122.2883616476824, 35.05769372687348]]]}
tier 2