title: Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).
abstract: Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.
authors: Natural Resource Ecology Laboratory, Colorado State University; amanda.west@colostate.edu.
authors: Amanda M
authors: AM
authors: West
authors: Natural Resource Ecology Laboratory, Colorado State University.
authors: Paul H
authors: PH
authors: Evangelista
authors: U.S. Geological Survey, Fort Collins Science Center.
authors: Catherine S
authors: CS
authors: Jarnevich
authors: Natural Resource Ecology Laboratory, Colorado State University.
authors: Nicholas E
authors: NE
authors: Young
authors: Natural Resource Ecology Laboratory, Colorado State University.
authors: Thomas J
authors: TJ
authors: Stohlgren
authors: U.S. Geological Survey, Fort Collins Science Center.
authors: Colin
authors: C
authors: Talbert
authors: U.S. Geological Survey - U.S. Department of the Interior, North Central Climate Science Center.
authors: Marian
authors: M
authors: Talbert
authors: U.S. Geological Survey - U.S. Department of the Interior, North Central Climate Science Center.
authors: Jeffrey
authors: J
authors: Morisette
authors: Natural Resource Ecology Laboratory, Colorado State University.
authors: Ryan
authors: R
authors: Anderson
mesh_terms: D017753:Ecosystem; D058865:Introduced Species; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D012984:Software; D032363:Tamaricaceae