Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/7052
Title: Development of landscape metrics to support process-driven ecological modeling
Authors: Regional Sediment Management Program (U.S.)
Reif, Molly K.
Swannack, Todd M.
Keywords: Ecological modeling
Landscape pattern
Engineering With Nature
Regional sediment management
Landscape metrics
Southwest Florida
Remote sensing
Geographic Information Systems
GIS
Regional Sediment Management Program (U.S.)
Publisher: Environmental Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: ERDC/EL TR ; 14-6.
Description: Technical Report
Abstract: Landscape pattern is an important driver in ecosystem dynamics and can control system-level functions such as nutrient cycling, connectivity, biodiversity, carbon sequestration, etc. Advances in remote sensing and Geographic Information Systems (GIS) have led to increased capability for quantifying landscape pattern, which is essential for relating spatial patterns to ecological processes. This study analyzes GIS and remote sensing data from two time periods, 2006 and 2010, over an approximately 2-km2 area in coastal Southwest Florida, to develop and examine landscape metrics with the goal of obtaining a better understanding of the factors that influence landscape changes observed as a result of the 2004 and 2005 hurricanes. Results are summarized by the change detection statistics and the landscape metrics, illustrating how quantitative measures can be applied to land cover data and assess both general land cover characteristics and underlying structure, aggregation, and shape characteristics. The landscape metrics analyses provided important indicators regarding the nature of the landscape changes and, thus, revealed important clues about the underlying ecological processes shaping them. This study represents an important first step in understanding how landscape metrics can be developed and in examining their potential use for linking spatial process to ecological pattern. More importantly, it also sets the stage for future research that will relate these findings to long-term ecological modeling and apply them to Engineering With Nature projects and Regional Sediment Management initiatives.
Rights: Approved for public release; distribution is unlimited.
URI: http://hdl.handle.net/11681/7052
Appears in Collections:Technical Report

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