caa a22 4500 467885567 CHVBK 20180406152733.0 cr unu---uuuuu 170328e20060401xx s 000 0 eng 10.1007/s10021-005-0119-1 doi (NATIONALLICENCE)springer-10.1007/s10021-005-0119-1 Map Misclassification Can Cause Large Errors in Landscape Pattern Indices: Examples from Habitat Fragmentation [Elektronische Daten] [William Langford, Sarah Gergel, Thomas Dietterich, Warren Cohen] Although habitat fragmentation is one of the greatest threats to biodiversity worldwide, virtually no attention has been paid to the quantification of error in fragmentation statistics. Landscape pattern indices (LPIs), such as mean patch size and number of patches, are routinely used to quantify fragmentation and are often calculated using remote-sensing imagery that has been classified into different land-cover classes. No classified map is ever completely correct, so we asked if different maps with similar misclassification rates could result in widely different errors in pattern indices. We simulated landscapes with varying proportions of habitat and clumpiness (autocorrelation) and then simulated classification errors on the same maps. We simulated higher misclassification at patch edges (as is often observed), and then used a smoothing algorithm routinely used on images to correct salt-and-pepper classification error. We determined how well classification errors (and smoothing) corresponded to errors seen in four pattern indices. Maps with low misclassification rates often yielded errors in LPIs of much larger magnitude and substantial variability. Although smoothing usually improved classification error, it sometimes increased LPI error and reversed the direction of error in LPIs introduced by misclassification. Our results show that classification error is not always a good predictor of errors in LPIs, and some types of image postprocessing (for example, smoothing) might result in the underestimation of habitat fragmentation. Furthermore, our results suggest that there is potential for large errors in nearly every landscape pattern analysis ever published, because virtually none quantify the errors in LPIs themselves. Springer Science+Business Media, Inc., 2006 Fragmentation nationallicence landscape metrics nationallicence landscape pattern indices nationallicence spatial error nationallicence classification error nationallicence thematic map nationallicence accuracy assessment nationallicence remote sensing nationallicence uncertainty nationallicence Langford William National Center for Ecological Analysis and Synthesis, 735 State St., Suite 300, 93101, Santa Barbara, California, USA aut Gergel Sarah Centre for Applied Conservation Research, Department of Forest Sciences, University of British Columbia, 3008 - 2424 Main Mall, V6T 1Z4, Vancouver, British Columbia, Canada aut Dietterich Thomas Computer Science Department, Oregon State University, 97331, Corvallis, Oregon, USA aut Cohen Warren USDA Forest Service, Pacific Northwest Research Station, 97331, Corvallis, Oregon, USA aut Ecosystems Springer-Verlag; www.springer-ny.com 9/3(2006-04-01), 474-488 1432-9840 9:3<474 2006 9 10021 https://doi.org/10.1007/s10021-005-0119-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10021-005-0119-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Langford William National Center for Ecological Analysis and Synthesis, 735 State St., Suite 300, 93101, Santa Barbara, California, USA aut NATIONALLICENCE 700 1- Gergel Sarah Centre for Applied Conservation Research, Department of Forest Sciences, University of British Columbia, 3008 - 2424 Main Mall, V6T 1Z4, Vancouver, British Columbia, Canada aut NATIONALLICENCE 700 1- Dietterich Thomas Computer Science Department, Oregon State University, 97331, Corvallis, Oregon, USA aut NATIONALLICENCE 700 1- Cohen Warren USDA Forest Service, Pacific Northwest Research Station, 97331, Corvallis, Oregon, USA aut NATIONALLICENCE 773 0- Ecosystems Springer-Verlag; www.springer-ny.com 9/3(2006-04-01), 474-488 1432-9840 9:3<474 2006 9 10021 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer