Influence of high-resolution data on the assessment of forest fragmentation

Abstract

Context Remote sensing has been a foundation of landscape ecology. The spatial resolution (pixel size) of remotely sensed land cover products has improved since the introduction of landscape ecology in the United States. Because patterns depend on spatial resolution, emerging improvements in the spatial resolution of land cover may lead to new insights about the scaling of landscape patterns. Objective We compared forest fragmentation measures derived from very high resolution (1 m²) data with the same measures derived from the commonly used (30 m × −30 m; 900 m²) Landsat-based data. Methods We applied area-density scaling to binary (forest; non-forest) maps for both sources to derive source-specific estimates of dominant (density ≥ 60%), interior (≥ 90%), and intact (100%) forest. Results Switching from low- to high-resolution data produced statistical and geographic shifts in forest spatial patterns. Forest and non-forest features that were “invisible” at low resolution but identifiable at high resolution resulted in higher estimates of dominant and interior forest but lower estimates of intact forest from the high-resolution source. Overall, the high-resolution data detected more forest that was more contagiously distributed even at larger spatial scales. Conclusion We anticipate that improvements in the spatial resolution of remotely sensed land cover products will advance landscape ecology through re-interpretations of patterns and scaling, by fostering new landscape pattern measurements, and by testing new spatial pattern-ecological process hypotheses.

  • Citation: Wickham, J.; Riitters, K. H. 2019. Influence of high-resolution data on the assessment of forest fragmentation. Landscape Ecology. 34: 2169-2182. 14 p. https://doi.org/10.1007/s10980-019-00820-z.
  • Keywords: Chesapeake Bay land cover Forest spatial patterns NLCD Spatial resolution remote sensing Introduction
  • Posted Date: September 16, 2019
  • Modified Date: January 23, 2020
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