@article {JRN54178, title = {Predicting risk of invasive species occurrence - remote-sensing strategies}, year = {2014}, pages = {59-77}, publisher = {US Department of Interior; US Geological Survey}, edition = {Scientific Investigations report 2012-5162}, chapter = {Chapter 6}, abstract = {

Remote sensing is a means to describe characteristics of an area without physically sampling the area. Remote sensors can be mounted on a satellite, plane, or other airborne structure. Remotely sensed data allow for landscape perspectives on management issues. Sensors measure the electromagnetic energy reflected from an object or area on the Earth{\textquoteright}s surface. These sensors measure energy at wavelengths that are beyond the range of human vision. The guiding principal is that different objects (for example, soils, plants, buildings, water) reflect and absorb light differently at varying wave{\textlnot}lengths. Graphically plotting the amount of radiation reflected at a given wavelength provides a unique signature for an object, especially if there is sufficient spectral resolution to distinguish its spectrum from those of other objects.\  Reflectance of clear water is typically low, with initial higher reflectance values in the blue end of the spectrum, which decreases as wavelength increases. Vegetation reflectance is typically low in both the blue and red regions of the spectrum due to absorption by chlorophyll. Because reflectance values peak at the green region, vegetation appears green. In the near infrared (NIR) region, reflectance is much higher than that in the visible bands due to leaf cellular structure. Therefore, vegetation can be identified by the high NIR but generally low visible reflectance. Spectral reflectance curves can be used to discriminate between vegetation types or plant species.

}, issn = {2012-5162}, doi = {10.3133/sir20125162}, url = {/files/bibliography/14-042.pdf}, author = {Kendal Young and Scott T. Schrader} } @article {JRN54179, title = {Spatial distribution and risk assessment of Johnsongrass (sorghum halepense) in Big Bend National Park, Texas}, year = {2014}, pages = {161-168}, publisher = {US Department of Interior; US Geological Survey}, chapter = {Scientific Invesigations Report 2012-5162}, abstract = {

We used Landsat 7 ETM+ imagery to illustrate how remotely sensed data can model predicted Johnsongrass habitat. We used spectral reflectance values for three seasons of data across 5 years (fall 1999, summer and fall 2000, spring and fall 2001, spring 2002, and spring 2003) to capture Johnsongrass vegetation phenology. Johnsongrass occurrences in BIBE consisted of 147 georeferenced localities.\ \  We used the program Maxent to predict Johnsongrass distributions. Maxent is a general approach for modeling species distributions using presence-only data sets. Maxent estimates a target probability distribution by finding the probability distribution of maximum entropy (the distribution that is most spread out, or closest to uniform). Maxent uses pixels with known species occurrence records to constitute the sample points. Remotely sensed and GIS data sets can provide environmental variables measured at each sample point. Analysis output includes a probabilistic interpretation, grading from least to most suitable habitat conditions.\  We evaluated the Johnsongrass predicted habitat model in Maxent by withholding 10 percent of the occurrence locations for testing. This approach, considered threshold-dependent, used a binomial test based on omission and predicted area. Model performance is evaluated using extrinsic omission rate and the proportion of all the pixels that are predicted as suitable habitat. The second approach to evaluating model performance is considered a threshold-independent procedure and uses receiver operating characteristic (ROC) curves. The advantage of ROC analysis is that area under the ROC curve (AUC) provides a single measure of model performance, independent of any particular choice of threshold. The AUC can be interpreted as the likelihood that habitat quality is correctly classified by the predictive model at randomly selected sites.

}, doi = {10.3133/sir20125162}, url = {/files/bibliography/14-043.pdf}, author = {Kendal Young and Scott T. Schrader} } @article {JRN54177, title = {Estimating suitable environments for invasive plant species across large landscapes: A remote sensing strategy using Landsat 7 ETM+}, journal = {International Journal of Biodiversity and Conservation}, volume = {5}, year = {2013}, month = {2013}, pages = {122-134}, chapter = {122}, abstract = {

The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed (Arundo donax), Lehmann lovegrass (Eragrostis lehmanniana), horehound (Marrubium vulgare) and buffelgrass (Pennisteum ciliare) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from \<1.0 to 18\%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with \<50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land manager

}, keywords = {invasive species, Landsat 7 ETM+, Maxent, presence-only modeling}, url = {/files/bibliography/13-040.pdf}, author = {Kendal Young and Laurie Abbott and Caldwell, Colleen and Scott T. Schrader} }