Characterizing Forest Fragments in Boreal, Temperate, and Tropical Ecosystems
Dates
Year
2008
Citation
Meddens, Arjan J. H., Hudak, Andrew T., Evans, Jeffrey S., Gould, William A., and González, Grizelle, 2008, Characterizing Forest Fragments in Boreal, Temperate, and Tropical Ecosystems: Ambio, v. 37, no. 7, p. 569-576.
Summary
An increased ability to analyze landscapes in a spatial manner through the use of remote sensing leads to improved capabilities for quantifying human-induced forest fragmentation. Developments of spatially explicit methods in landscape analyses are emerging. In this paper, the image delineation software program eCognition and the spatial pattern analysis program FRAGSTATS were used to quantify patterns of forest fragments on six landscapes across three different climatic regions characterized by different moisture regimes and different influences of human pressure. Our results support the idea that landscapes with higher road and population density are more fragmented; however, there are other, equally influential factors contributing [...]
Summary
An increased ability to analyze landscapes in a spatial manner through the use of remote sensing leads to improved capabilities for quantifying human-induced forest fragmentation. Developments of spatially explicit methods in landscape analyses are emerging. In this paper, the image delineation software program eCognition and the spatial pattern analysis program FRAGSTATS were used to quantify patterns of forest fragments on six landscapes across three different climatic regions characterized by different moisture regimes and different influences of human pressure. Our results support the idea that landscapes with higher road and population density are more fragmented; however, there are other, equally influential factors contributing to fragmentation, such as moisture regime, historic land use, and fire dynamics. Our method provided an objective means to characterize landscapes and assess patterns of forest fragments across different forested ecosystems by addressing the limitations of pixel-based classification and incorporating image objects.
