Geographic Information System Variables Research Articles

Motorized Activity on Legacy Seismic Lines: A Predictive Modeling Approach to Prioritize Restoration Efforts.

Natural regeneration of seismic lines, cleared for hydrocarbon exploration, is slow and often hindered by vegetation damage, soil compaction, and motorized human activity. There is an extensive network of seismic lines in western Canada which is known to impact forest ecosystems, and seismic lines have been linked to declines in woodland caribou (Rangifer tarandus caribou). Seismic line restoration is costly, but necessary for caribou conservation to reduce cumulative disturbance. Understanding where motorized activity may be impeding regeneration of seismic lines will aid in prioritizing restoration. Our study area in west-central Alberta, encompassed five caribou ranges where restoration is required under federal species at risk recovery strategies, hence prioritizing seismic lines for restoration is of immediate conservation value. To understand patterns of motorized activity on seismic lines, we evaluated five a priori hypotheses using a predictive modeling framework and Geographic Information System variables across three landscapes in the foothills and northern boreal regions of Alberta. In the northern boreal landscape, motorized activity was most common in dry areas with a large industrial footprint. In highly disturbed areas of the foothills, motorized activity on seismic lines increased with low vegetation heights, relatively dry soils, and further from forest cutblocks, while in less disturbed areas of the foothills, motorized activity on seismic lines decreased proportional to seismic line density, slope steepness, and white-tailed deer abundance, and increased proportional with distance to roads. We generated predictive maps of high motorized activity, identifying 21,777 km of seismic lines where active restoration could expedite forest regeneration.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses.

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data. We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

Modelación espacial de área basal y volumen de madera en bosques manejados de Pinus patula y P. teocote en el ejido Atopixco, Hidalgo

Se modeló el área basal (AB m2/ha) y volumen de madera (VOL m3/ha) de rodales coetáneos de Pinus patula y P. teocote localizados en Hidalgo, México, utilizando variables de porcentaje de cobertura arbórea (COB), índice de área foliar en sitio (LAI5 m2/m2) y alométrico (LAIAL m2/m2), variables cartográficas y de reflectancia obtenidas de una imagen multiespectral del satélite SPOT 5 . Para la caracterización de las condiciones ambientales se digitalizó la carta de Efectos Climáticos (escala 1: 250 000) de INEGI y el Modelo Digital de Elevación (MDE) correspondiente a la zona. Se integró un Sistema de Información Geográfica (SIG) constituido por las siguientes capas de información: altitud, pendiente, exposición, temperaturas, precipitaciones, bandas espectrales, y dos grupos de índices de vegetación basados en a) la relación espectral de bandas (NDVI - CTVI) y b) la distancia espectral entre vegetación y suelo desnudo (PVI – SAVI) . Con datos de campo y del SIG se ajustaron modelos de regresión lineal múltiple. Para el AB la relación más significativa fue con LAIAL, Temperatura Media Anual (TMA), ALTITUD y la Banda 4 de SPOT (P < 0,001, R2 = 0,9662). Para el VOL la relación más significativa fue con LAIAL, Temperatura Media Anual (TMA) y ALTITUD (P < 0,001, R2 = 0,8856).

Linking Forest Inventory and Analysis Ground Data to Maps: An Example for Nevada Pinyon-Juniper Forests

Using data from the USDA Forest Service's Forest Inventory and Analysis (FIA) program, we developed a mapping tool for displaying statistical forestland information. To develop this technique, we used data from Nevada's pinyon–juniper dryland forests, a major component of Nevada's forest cover important for food and wood sources, wildlife cover, and carbon storage. Land managers must grapple with issues, such as the health of these forests, the explanation of their encroachment into grasslands and shrublands, and causes of pinyon mortality associated with drought and bark-beetle activity. Our methodology can assist managers by offering the potential to map FIA statistics, such as carbon, volume, and stand structure to local conditions. FIA plots were classified according to the basal area occupied by dominant species groups. Employing this classification, we modeled the probability of pinyon–juniper occurrence at stand scale in Nevada from FIA data, using readily available remote sensing and geographic information system variables. Then, using the model with a Nevada-specific classification rule, we produced a cover map for the state. Finally, we linked our model classification rule to FIA summary data to create a detailed statistical map of Nevada forest carbon distribution—a visual representation of FIA data on the landscape.

Channel substrate prediction from GIS for habitat estimation in Lake Erie tributaries

The conservation of ecologically and economically important species, as well as the management of invasive species, benefits from the ability to make broad-scale predictions of habitat. In this paper, we revised an existing substrate size model based upon stream power to include variables that are readily-quantifiable in a Geographic Information System (GIS) (i.e. stream slope and drainage area). We found no significant difference between slopes measured in the field using surveying techniques and slopes measured in a GIS using a 10 m digital elevation model and high resolution stream dataset. GIS-derived drainage areas and those measured with hand-delineations were also statistically similar. The revised model can be applied using both GIS and field-derived variables to predict median particle sizes from stream power in northeastern Ohio streams draining to Lake Erie. Integration of such models into a GIS could result in regional estimates of the amount and location of preferred fish habitat, which has important applications in fisheries management. In particular, we provide examples of how the predictive substrate model could improve assessment methodologies for invasive sea lamprey, thereby improving eradication measures, and how we may better understand geographic linkages between walleye spawning and nursery habitats.

Trade‐offs between specificity and regional generality in habitat association models: a case study of two species of African vulture

Summary 1. The habitat requirements of various species have been evaluated by statistical models. However, recent studies have shown that models are often not transferable between regions, limiting their applicability and ability to inform management decisions. One possible cause is that models tend to reflect dominant landscape features, which vary between regions. Transferability, and thus applicability, may be increased by developing models from multiple regions. 2. We addressed this via a case study of two vulture species (white‐backed and lappet‐faced vultures, Gyps africanus and Aegypius tracheliotos) from six biogeographically different regions across southern Africa. Logistic models, developed using an information‐theoretic approach, were used to predict nest occurrence based on explanatory variables derived from a Geographic Information System (GIS), the usual method for species with large ranges. Variables reflected key requirements at different spatial scales: food availability, human disturbance and nesting trees. We developed models using data from single and multiple regions, and tested the cross‐regional transferability. We also collected field data to asses the adequacy of the GIS variables. 3. There was a significant negative correlation between specificity and regional generality, multi‐region models tending to be more consistently transferable than single‐region models but having a weaker fit within the regions where they were developed. Multi‐region models of nesting habitat were more structurally similar to each other than single‐region models. GIS variables adequately represented the landscape but with differing adequacy between regions. There were no observed fitness benefits to the observed site selection. 4. Synthesis and applications. Models of species distribution are not transferable between regions, and use of models to inform management decisions in regions other than that used for model development should be undertaken with caution. Models are often built using GIS predictors only broadly related to the landscape properties of interest and the adequacy of such proxies can vary between regions, leading to models that emphasize dominant landscape features. Models developed from multiple regions partially overcome this problem by identifying predictors that apply across many regions and are more transferable. However, this increased generality trades off against reduced specificity. Models should be constructed with consideration to their intended use.

Castor canadensis (Beaver) Impoundment Associated with Geomorphology of Southeastern Streams

Abstract We used a geographic information system (GIS) and logistic regression to investigate relationships between geomorphology and Castor canadensis (North American beaver) impoundment of lower-order, blackwater streams of a southeastern landscape. Using GIS, we divided streams into 632 500-m reaches and measured a set of geomorphic variables for each reach. Beavers were most likely to impound stream reaches crossed by roads with a gradient of ≈ 0.6 to 1.2% and watershed sizes of ≈ 2500 ha; reaches with watershed sizes 5000 ha were almost completely avoided. Gradient and road crossings contributed little to discrimination among impounded and unimpounded reaches, suggesting these variables had relatively small influences on beaver impoundment when compared to stream size. Our results indicate that GIS and geomorphic variables can be used to model the impoundment of streams over larger areas (e.g., the proportion of third-order watersheds impounded), but are less accurate at predicting th...

Making History Count: The Guadalajara Census Project (1791–1930)

Making History Count: The Guadalajara Census Project (1791–1930)

Use of GIS and ancillary variables to predict volatile organic compound and nitrogen dioxide levels at unmonitored locations

In late 1999, passive air sampling of nitrogen dioxide (NO 2) and volatile organic compounds was conducted at 22 school locations and two intensive sites in El Paso, Texas. Our goal was to predict concentrations of NO 2 and benzene, toluene, ethylbenzene, o-xylene, and m, p-xylene at a total of 55 schools. The predictive equations were developed by regressing the passive monitor measurements at the 22 monitored schools on land-use variables derived from a geographic information system (GIS). These GIS-based ancillary variables included distance to the nearest border crossing, elevation, population density, distance to roads with specified traffic volumes, traffic intensity around the schools, and distance to the nearest petroleum facility. The reliability of the predictive equations was assessed at the two intensive monitoring sites. For all pollutants, the most useful predictive ancillary variables were elevation, population density, distance to a border crossing, and distance to a petroleum facility. For estimating NO 2, traffic intensity was also important.

A Multiscaled Model of Southwestern Willow Flycatcher Breeding Habitat

The southwestern willow flycatcher (SWFL; Empidonax traillii extimus) is an endangered songbird whose habitat has declined dramatically over the last century. Understanding habitat selection patterns and the ability to identify potential breeding areas for the SWFL is crucial to the management and conservation of this species. We developed a multiscaled model of SWFL breeding habitat with a Geographic Information System (GIS), survey data, GIS variables, and multiple logistic regressions. We obtained presence and absence survey data from a river-ine ecosystem and a reservoir delta in south-central Arizona, USA, in 1999. We extracted the GIS variables from satellite imagery and digital elevation models to characterize vegetation and floodplain within the project area. We used multiple logistic regressions within a cell-based (30 x 30 m) modeling environment to (1) determine associations between GIS variables and breeding-site occurrence at different spatial scales (0.09-72 ha), and (2) construct a predictive model. Our best model explained 54% of the variability in breeding-site occurrence with the following variables: vegetation density at the site (0.09 ha), proportion of dense vegetation and variability in vegetation density within a 4.5-ha neighborhood, and amount of floodplain or flat terrain within a 41-ha neighborhood. The density of breeding sites was highest in areas that the model predicted to be most suitable within the project area and at an external test site 200 km away. Conservation efforts must focus on protecting not only occupied patches, but also surrounding riparian forests and floodplain to ensure long-term viability of SWFL. We will use the multiscaled model to map SWFL breeding habitat in Arizona, prioritize future survey effort, and examine changes in habitat abundance and quality over time.