Paul Neville

HABITAT USE AND NEST SITE SELECTION BY NESTING LESSER PRAIRIE-CHICKENS IN SOUTHEASTERN NEW MEXICO

Abstract Lesser prairie-chickens (Tympanuchus pallidicinctus) occur in shinnery oak (Quercus havardii) and sand sagebrush (Artemisia filifolia) grassland habitats in New Mexico, Texas, Oklahoma, Kansas, and Colorado. Range-wide population reductions since the 1800s have been attributed to habitat loss, especially of nesting habitat. Using radio-telemetry and a vegetation map of the study area, we investigated habitat use by lesser prairie-chicken hens during the nesting season in herbicide-treated and untreated pastures (each about 1,000 ha in size). Herbicide treatment was effective in reducing shinnery oak cover. The most common vegetation types in hen home ranges were those dominated by shinnery oak. Hens were detected more often than randomly in or near untreated pastures. Although hens were detected in both treated and untreated habitats, 13 of 14 nests were located in untreated pastures, and all nests were located in areas dominated by shinnery oak. Areas immediately surrounding nests had higher shrub composition than the surrounding pastures. This study suggests that herbicide treatment to control shinnery oak might adversely impact nesting lesser prairie-chickens.

Potential environmental and economic impacts of turfgrass in Albuquerque, New Mexico (USA)

We estimated the ecological and economic impact of urban turfgrass production in a large city. A satellite image was used to evaluate the turfgrass area of Albuquerque, New Mexico, U.S.A. Turfgrass, the major vegetation component of the city, covers 7,650 ha and represents approximately 30.0% of the metropolitan area. Of the total grass area, 85.0% exists as home lawns, 8.3% occurs in parks, and 6.7% is on golf courses.We estimated that turfgrass uses an average of 475,000 m3 of water every day, yielding more than 4,575,000 kg of grass clippings going to the landfill in approximately 250,000 garbage bags each year. The approximate yearly cost of maintenance comes to more than

GIS habitat analysis for lesser prairie-chickens in southeastern New Mexico

30 million which includes the potential purchase of 322,065 kg of nitrogen fertilizer, 286,110 kg of phosphorus fertilizer, 237,915 kg of potassium fertilizer and 37,408 kg of active ingredients of insecticides.Our evaluation of the cumulative effects of domestic and municipal turfgrass production can guide the application of economically sound Integrated Pest Management strategies and enable planning for sustained use of potentially limiting resources, such as water, in semiarid environments.

Multispectral Landsat image classification using a data clustering algorithm

BackgroundWe conducted Geographic Information System (GIS) habitat analyses for lesser prairie-chicken (LPCH, Tympanuchus pallidicinctus) conservation planning. The 876,799 ha study area included most of the occupied habitat for the LPCH in New Mexico. The objectives were to identify and quantify: 1. suitable LPCH habitat in New Mexico, 2. conversion of native habitats, 3. potential for habitat restoration, and 4. unsuitable habitat available for oil and gas activities.ResultsWe found 16% of suitable habitat (6% of the study area) distributed in 13 patches of at least 3,200 ha and 11% of suitable habitat (4% of the study area) distributed in four patches over 7,238 ha. The area converted from native vegetation types comprised 17% of the study area. Ninety-five percent of agricultural conversion occurred on private lands in the northeastern corner of the study area. Most known herbicide-related conversions (82%) occurred in rangelands in the western part of the study area, on lands managed primarily by the US Bureau of Land Management (BLM). We identified 88,190 ha (10% of the study area) of habitats with reasonable restoration potential. Sixty-two percent of the primary population area (PPA) contained occupied, suitable, or potentially suitable habitat, leaving 38% that could be considered for oil and gas development.ConclusionAlthough suitable LPCH habitat appears at first glance to be abundant in southeastern New Mexico, only a fraction of apparently suitable vegetation types constitute quality habitat. However, we identified habitat patches that could be restored through mesquite control or shin-oak reintroduction. The analysis also identified areas of unsuitable habitat with low restoration potential that could be targeted for oil and gas exploration, in lieu of occupied, high-quality habitats. Used in combination with GIS analysis and current LPCH population data, the habitat map represents a powerful conservation and management tool.

Extracting Pavement Surface Distress Conditions Based on High Spatial Resolution Multispectral Digital Aerial Photography

This work presents a new application of a data-clustering algorithm in Landsat image classification, which improves on conventional classification methods. Neural networks have been widely used in Landsat image classification because they are unbiased by data distribution. However, they need long training times for the network to get satisfactory classification accuracy. The data-clustering algorithm is based on fuzzy inferences using radial basis functions and clustering in input space. It only passes training data once so it has a short training tune. It can also generate fuzzy classification, which is appropriate in the case of mixed, intermediate or complex cover pattern pixels. This algorithm is applied in the land cover classification of Landsat 7 ETM+ over the Rio Rancho area, New Mexico. It is compared with back-propagation neural network (BPNN) to illustrate its effectiveness and concluded that it can get a better classification using shorter training time.

Characterizing Pavement Surface Distress Conditions with Hyper-Spatial Resolution Natural Color Aerial Photography

Abstract State transportation agencies regularly collect data on pavement surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair decisions. Routinely-acquired and publically-available high spatial resolution (hsr) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regression models were used to evaluate the potential of using HSR multispectral digital aerial photographs to estimate pavement surface overall distress conditions. Various models were developed using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R 2 > 0.95), even when using only half of the sample sites (R 2 > 0.92). These results indicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.

Hierarchical Fuzzy Classification of Remote Sensing Data

Roadway pavement surface distress information is critical for effective pavement asset management, and subsequently, transportation management agencies at all levels (i.e., federal, state, and local) dedicate a large amount of time and money to routinely evaluate pavement surface distress conditions as the core of their asset management programs. However, currently adopted ground-based evaluation methods for pavement surface conditions have many disadvantages, like being time-consuming and expensive. Aircraft-based evaluation methods, although getting more attention, have not been used for any operational evaluation programs yet because the acquired images lack the spatial resolution to resolve finer scale pavement surface distresses. Hyper-spatial resolution natural color aerial photography (HSR-AP) provides a potential method for collecting pavement surface distress information that can supplement or substitute for currently adopted evaluation methods. Using roadway pavement sections located in the State of New Mexico as an example, this research explored the utility of aerial triangulation (AT) technique and HSR-AP acquired from a low-altitude and low-cost small-unmanned aircraft system (S-UAS), in this case a tethered helium weather balloon, to permit characterization of detailed pavement surface distress conditions. The Wilcoxon Signed Rank test, Mann-Whitney U test, and visual comparison were used to compare detailed pavement surface distress rates measured from HSR-AP derived products (orthophotos and digital surface models generated from AT) with reference distress rates manually collected on the ground using standard protocols. The results reveal that S-UAS based hyper-spatial resolution imaging and AT techniques can provide detailed and reliable primary observations suitable for characterizing detailed pavement surface distress conditions comparable to the ground-based manual measurement, which lays the foundation for the future application of HSR-AP for automated detection and assessment of detailed pavement surface distress conditions.

Design and test of an object-oriented GIS to map plant species in the Southern Rockies

Land covers mix and high input dimension are two important issues to affect the classification accuracy of remote sensing images. Fuzzy classification has been developed to represent the mixture of land covers. Two fuzzy classifiers of Fuzzy Rule-Based (FRB) and Fuzzy Neural Network (FNN) were studied to illustrate the interpretability of fuzzy classification. A hierarchical structure was proposed to simply multi-class classification to multiple binary classification to reduce computation time caused by high number of inputs. The classifiers were compared on the land cover classification of a Landsat 7 ETM+ image over Rio Rancho, New Mexico, and it was proved that Hierarchical Fuzzy Neural Network (HFNN) classifier is the best combination of better classification accuracy with shorter CPU time requirement.

Four-dimensional terrain model for tracking floristic changes induced by climate warming

Abstract Elevational and latitudinal shifts occur in the flora of the Rocky Mountains due to long term climate change. In order to specify which species are successfully migrating with these changes, and which are not, an object‐oriented, image‐based geographic information system(GIS) is being created to animate evolving ecological regimes of temperature and precipitation. Research at the Earth Data Analysis Center (EDAC) is developing a landscape model that includes the spatial, spectral and temporal domains. It is designed to visualize migratory changes in the Rocky Mountain flora, and to specify future community compositions. The object‐oriented database will eventually tag each of the nearly 6000 species with a unique hue, intensity, and saturation value, so their movements can be individually traced. An associated GIS includes environmental parameters that control the distribution of each species in the landscape, and satellite imagery is used to help visualize the terrain. Polygons for the GIS are d...

Indices of grassland biodiversity in the Chihuahuan Desert Ecoregion derived from remote sensing

Elevational and latitudinal shifts will occur in the flora and vegetation of the Rocky Mountains due to climate warming. If we are to specify which species are successfully migrating in tune with these changes, and which are being adversely impacted, a 4-dimensional image-based GIS is required to visualize and animate new ecological regimes imposed by changing temperature and precipitation patterns. Research at TAC aims to develop a new algorithm for a terrain model that includes the spatial, spectral, and temporal domains. It is designed to visualize changes in the Rocky Mountain flora, and to specify the predicted community compositions at any future time. The strategy is to assign unique hue, intensity, and saturation values for each of the nearly 6000 species comprising the flora of the Rockies. Hue is assigned on the basis of elevational zone; intensity on the basis of slope and aspect; and saturation, on the basis of abundance. Polygons for an associated GIS are delineated as landform facets that are expected to be stable in ecological time (i.e., over the next few thousand years). The analysis then assesses the gradual progression of species as they migrate upslope and poleward through these polygons. At any future year over the next several thousand, the modeling process can be stopped to assess both the rate and directions of change, as well as the species composition of each plant community occupying a specific polygon.