Publications

@article{Chen2015,
title = {Mapping burn severity in a disease-impacted forest landscape using Landsat and MASTER imagery},
author = {Gang Chen and Margaret R. Metz and David M. Rizzo and Ross K. Meentemeyer},
url = {http://www.sciencedirect.com/science/article/pii/S0303243415000847},
doi = {dx.doi.org/10.1016/j.jag.2015.04.005},
year = {2015},
date = {2015-04-22},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {40},
pages = {91-99},
abstract = {Global environmental change has increased forest vulnerability to the occurrence of interacting disturbances, including wildfires and invasive diseases. Mapping post-fire burn severity in a disease-affected forest often faces challenges because burned and infested trees may exhibit a high similarity in spectral reflectance. In this study, we combined (pre- and post-fire) Landsat imagery and (post-fire) high-spectral resolution airborne MASTER data [MODIS (moderate resolution imaging spectroradiometer)/ASTER (advanced spaceborne thermal emission and reflection radiometer)] to map burn severity in a California coastal forest environment, where a non-native forest disease sudden oak death (SOD) was causing substantial tree mortality. Results showed that the use of Landsat plus MASTER bundle performed better than using the individual sensors in most of the evaluated forest strata from ground to canopy layers (i.e., substrate, shrubs, intermediate-sized trees, dominant trees and average), with the best model performance achieved at the dominant tree layer. The mid to thermal infrared spectral bands (3.0–12.5 μm) from MASTER were found to augment Landsat’s visible to shortwave infrared bands in burn severity assessment. We also found that infested and uninfested forests similarly experienced moderate to high degrees of burns where CBI (composite burn index) values were higher than 1. However, differences occurred in the regions with low burn severity (CBI values lower than 1), where uninfested stands revealed a much lower burn effect than that in infested stands, possibly due to their higher resilience to small fire disturbances as a result of higher leaf water content.},
keywords = {burn severity, forest fire, interacting disturbances, invasion, Landsat, MASTER, sudden oak death},
pubstate = {published},
tppubtype = {article}
}

@article{Singh2015b,
title = {Detecting understory plant invasion in urban forests using LiDAR},
author = {Kunwar K. Singh and Amy J. Davis and Ross K. Meentemeyer},
url = {http://www.sciencedirect.com/science/article/pii/S0303243415000203},
doi = {doi:10.1016/j.jag.2015.01.012},
year = {2015},
date = {2015-02-12},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {38},
pages = {267-279},
abstract = {Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features – optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense.},
keywords = {Chinese privet, IKONOS, invasion, LiDAR, Ligustrum sinense, random forest},
pubstate = {published},
tppubtype = {article}
}

@article{Wine2015,
title = {Understanding Human–Coyote Encounters in Urban Ecosystems Using Citizen Science Data: What Do Socioeconomics Tell Us?},
author = {Stuart Wine and Sara A. Gagne and Ross K. Meentemeyer},
url = {https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0CCcQFjAB&url=http%3A%2F%2Flink.springer.com%2Fcontent%2Fpdf%2F10.1007%252Fs00267-014-0373-0.pdf&ei=JhGLVfqPIYyXgwT12IPABQ&usg=AFQjCNG8H3daXCGncVE8kIdFIGrMCUoEkw&sig2=psG_d-L74KjJ3i00HYqkrg&bvm=bv.96339352,d.eXY},
doi = {10.1007/s00267-014-0373-0},
issn = {1432-1009},
year = {2015},
date = {2015-01-01},
journal = {Environmental Management},
volume = {55},
number = {1},
pages = {159-170},
abstract = {The coyote (Canis latrans) has dramatically expanded its range to include the cities and suburbs of the western US and those of the Eastern Seaboard. Highly adaptable, this newcomer’s success causes conflicts with residents, necessitating research to understand the distribution of coyotes in urban landscapes. Citizen science can be a powerful approach toward this aim. However, to date, the few studies that have used publicly reported coyote sighting data have lacked an in-depth consideration of human socioeconomic variables, which we suggest are an important source of overlooked variation in data that describe the simultaneous occurrence of coyotes and humans. We explored the relative importance of socioeconomic variables compared to those describing coyote habitat in predicting human–coyote encounters in highly-urbanized Mecklenburg County, North Carolina, USA using 707 public reports of coyote sightings, high-resolution land cover, US Census data, and an autologistic multi-model inference approach. Three of the four socioeconomic variables which we hypothesized would have an important influence on encounter probability, namely building density, household income, and occupation, had effects at least as large as or larger than coyote habitat variables. Our results indicate that the consideration of readily available socioeconomic variables in the analysis of citizen science data improves the prediction of species distributions by providing insight into the effects of important factors for which data are often lacking, such as resource availability for coyotes on private property and observer experience. Managers should take advantage of citizen scientists in human-dominated landscapes to monitor coyotes in order to understand their interactions with humans.},
keywords = {autologistic regression, citizen science, crowdsourcing, human-wildlife conflict, invasion, species distribution model, urban wildlife},
pubstate = {published},
tppubtype = {article}
}

@article{Hohl2014,
title = {Go with the flow: geospatial analytics to quantify hydrologic landscape connectivity for passively dispersed microorganisms},
author = {Alexander Hohl and Tomas Vaclavik and Ross K. Meentemeyer},
url = {http://dx.doi.org/10.1080/13658816.2013.854900},
doi = {10.1080/13658816.2013.854900},
year = {2014},
date = {2014-01-01},
journal = {International Journal of Geographical Information Science},
volume = {28},
number = {8},
pages = {1626-1641},
abstract = {Understanding the diverse ways that landscape connectivity influences the distribution of microbial species is central to managing the spread and persistence of numerous biological invasions. Here, we use geospatial analytics to examine the degree to which the hydrologic connectivity of landscapes influences the transport of passively dispersed microbes, using the invasive plant pathogen Phytophthora ramorum as a case study. Pathogen occurrence was analyzed at 280 stream baiting stations across a range of watersheds – exposed to variable inoculum pressure – in California over a 7-year period (2004–2010). Using logistic regression, we modeled the probability of pathogen occurrence at a baiting station based on nine environmental variables. We developed a novel geospatial approach to quantify the hydrologic connectivity of host vegetation and inoculum pressure derived from least cost distance analyses in each watershed. We also examined the influence of local environmental conditions within the immediate neighborhood of a baiting station. Over the course of the sampling period, the pathogen was detected at 67 baiting stations associated with coastal watersheds with mild climate conditions, steep slopes, and higher levels of inoculum pressure. At the watershed scale, hydrologic landscape connectivity was a key predictor of pathogen occurrence in streams after accounting for variation in climate and exposure to inoculum. This study illustrates a geospatial approach to modeling the degree to which hydrologic systems play a role in shaping landscape structures conducive for the transport of passively dispersed microbes in heterogeneous watersheds.},
keywords = {cost distance, disease, epidemiology, functional connectivity, hydrology, invasion},
pubstate = {published},
tppubtype = {article}
}