Mark Finco

Modeling Percent Tree Canopy Cover: A Pilot Study

Tree canopy cover is a fundamental component of the landscape, and the amount of cover influences fire behavior, air pollution mitigation, and carbon storage. As such, efforts to empirically model percent tree canopy cover across the United States are a critical area of research. The 2001 national-scale canopy cover modeling and mapping effort was completed in 2006, and here we present results from a pilot study for a 2011 product. We examined the influence of two different modeling techniques (random forests and beta regression), two different Landsat imagery normalization processes, and eight different sampling intensities across five different pilot areas. We found that random forest out-performed beta regression techniques and that there was little difference between models developed based on the two different normalization techniques. Based on these results we present a prototype study design which will test canopy cover modeling approaches across a broader spatial scale.

Modeling dense gaseous contaminant pathways over complex terrain using a geographic information system

An impedance surface approach within a geographic information system (GIS) is used to model dispersion pathways for dense, gaseous, hazardous contaminants across complex terrain. The impedance surface methodology is tested using the Nogales Arizona-Sonora region of the US-Mexico border for simulation under varying wind conditions. This approach provides a realistic approximation of potential dispersion patterns in complex terrain where most existing dispersion models are inappropriate.

Spatial Search and Spatial Competition: A Probability Analysis of Basic Results from the Spatially-Restricted Theory

Existing analyses of the interactions between spatial search and spatial competition are limited since it has been difficult to incorporate the complex routing component of the behavior. This paper examines the interactions between spatial search behavior and spatial competition using a probabilistic modeling strategy that doesnot restrict routing. Experimental analyses examine basic hypotheses from optimal search theory and competitive location theory in the context of spatial search. While experimental results support several of these basic hypotheses, the experiments also generate some contradictions and additional insights.

Investigating US-Mexico Border Community Vulnerability to Industrial Hazards: A Simulation Study in Ambos Nogales

A communitys vulnerability to industrial hazards is a function of human settlement patterns, demographics, and physical characteristics of the hazard. At both the conceptual and practical levels these factors must be linked in space and time to fully understand the environmental impact of a hazard on the population. This paper develops a methodology which analyzes community vulnerability by integrating a population vulnerability model with a model of dense gas dispersion using a common geographic information system framework. A study area along the U.S.-Mexico border, Ambos Nogales, is used to demonstrate the effectiveness of this approach. Two industrial locations are compared. The results show that geographic proximity to a hazard is a necessary, but not always sufficient, measure of the potential impact of a hazard on the community. Based on these results, suggestions concerning land-use policy and emergency response planning have been made for industrial locations in Ambos Nogales.

Near-Real Time Delivery of MODIS-Based Information on Forest Disturbances

The Real-Time Forest Disturbance (RTFD) program of the Forest Service, U.S. Department of Agriculture (USFS) provides timely spatial information regarding changes in forest conditions to the Forest Health Protection (FHP) and State and Private Forestry (S&PF) community for improving aerial detection and forest health survey efficiency. The USFS Remote Sensing Applications Center (RSAC) creates CONUS-wide forest change geospatial layers for the RTFD program every 8 days during the growing season using image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and delivers these data to a web mapping application named the Forest Disturbance Monitor (FDM) developed by the USFS Forest Health Technology Enterprise Team (FHTET).