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Jenkins, R.B. and Frazier, P.S. (2010) High-Resolution Remote Sensing of Upland Swamp Boundaries and Vegetation for Baseline Mapping and Monitoring. Wetlands, 30, 531-540.
https://doi.org/10.1007/s13157-010-0059-1

has been cited by the following article:

  • TITLE: Modeling and Mapping Forest Floor Distributions of Common Bryophytes Using a LiDAR-Derived Depth-to-Water Index

    AUTHORS: Monique Goguen, Paul A. Arp

    KEYWORDS: Bryophytes, Wet Areas, Macro- and Micro-Topography, Forest Floor, Forest Litter, Mound And Pit, Canopy Closure, Digital Elevation Modeling, Logistic Regression

    JOURNAL NAME: American Journal of Plant Sciences, Vol.8 No.4, March 31, 2017

    ABSTRACT: This article describes how the cartographic depth-to-water (DTW) index in combination with other variables can be used to quantify, model and map the distribution of common forest floor bryophytes, at 1 m resolution. This was done by way of a case study, using 12 terrain and climate representative locations across New Brunswick, Canada. The presence/absence by moss species was determined at each location along upland-to-wetland transects within >10-m spaced 1-m2 forest floor plots. It was found that Bazzania trilobata, Dicranum polysetum, Polytrichum commune, Hylocomium splendens, and Pleurozium schreberi had greater probabilities of occurrence in well-drained forested areas, whereas Sphagnum fuscum and Sphagnum girgensohnii dominated in low-lying wet areas. The presence/absence of each species was quantified by way of logistic regression analyses, using DTW, slope, canopy closure, forest litter depth, ecosite type (8 classes), nutrient regime (4 classes, poor to rich); vegetation type (deciduous, coniferous, mixed, and shrubs), and macro- and micro-topography (upland, wetland; mounds, pits) as predictor variables. Among these, log10DTW and forest litter depth were the most consistent predictor variables, followed by mound versus pit. For the mapping purpose, only log10DTW and already mapped classifications for upland versus wetland and vegetation type were used to predict the probability of occurrences for the most frequent moss species, namely, D. polysetum, P. schreberi and Sphagnum spp. The overall accuracy for doing this ranged from 67% to 83%, with false positives and negatives amounting to 18% to 42%. The overall classification accuracy exceeded the probability by chance alone at 76.8%, with the significance level reached at 75.3%. The average level of probability by chance alone was 60.3%.