Tracking the Rapid Pace of GIS-Related Capabilities and Their Accessibility

Sampson, Barry A.; DelGiudice, Glenn D.

doi:10.2193/0091-7648(2006)34[1446:ttrpog]2.0.co;2

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Much literature focuses on theory and modeling in distributed decision‐making; little addresses the issue from an operational standpoint (notable exceptions include Williams et al []). Technological advances in data management provide opportunities to better inform natural resource management decisions, and to deepen our understanding of natural systems (Sampson and DelGiudice ). However, because the technological component of natural resource monitoring and management projects is usually not focal, it is often glossed over in the literature, providing inadequate guidance for managers (Sampson and DelGiudice ).…”

Section: Data Management Challengesmentioning

confidence: 99%

A data management system for long‐term natural resource monitoring and management projects with multiple cooperators

Hunt

Jacobi²,

Knutson

et al. 2015

Wildl. Soc. Bull.

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Collection, processing, and long-term storage of data for broad-scale, collaborative natural resource monitoring and management projects poses technical and administrative challenges that, if not properly addressed, result in suboptimal management and learning. Data from many cooperators, often spanning multiple organizations, must be efficiently centralized and processed, and must be consistent in content and quality over the lifespan of such projects. We present a data management system for natural resource monitoring and management consisting of 2 components: a centralized, web-based platform for data entry and a connected relational database for data processing, modeling, and analysis. After the data management system has been customized to meet the needs of a specific project, operation and system maintenance require minimal external technical support, making it suitable for long-term projects that face potential staffing and budgeting constraints. We discuss the scope of projects for which this approach is applicable and document 2 U.S. Fish and Wildlife Service adaptive management case studies demonstrating this data management system: 1) Native Prairie Adaptive Management, and 2) Wetland Restoration and Sediment Removal. The standardized approach presented within is broadly applicable in collaborative natural resource monitoring and management settings and has the potential to improve management outcomes and facilitate deeper ecological understanding of systems being managed.

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Section: Data Management Challengesmentioning

confidence: 99%

A data management system for long‐term natural resource monitoring and management projects with multiple cooperators

Hunt

Jacobi²,

Knutson

et al. 2015

Wildl. Soc. Bull.

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“…1999, Kernohan et al. 2001, Selkirk and Bishop 2002, Sampson and Delgiudice 2006). However, Laver and Kelly (2008) noted that there is no common consensus as to the most accurate or acceptable way to compute home range estimates.…”

Section: Introductionmentioning

confidence: 99%

A Characteristic‐Hull Based Method for Home Range Estimation

Downs

Horner

2009

Transactions in GIS

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Recent literature has reported inaccuracies associated with some popular home range estimators such as kernel density estimation, especially when applied to point patterns of complex shapes. This study explores the use of characteristic hull polygons (CHPs) as a new method of home range estimation. CHPs are special bounding polygons created in GIS that can have concave edges, be composed of disjoint regions, and contain areas of unoccupied space within their interiors. CHPs are created by constructing the Delaunay triangulation of a set of points and then removing a subset of the resulting triangles. Here, CHPs consisting of 95% of the smallest triangles, measured in terms of perimeter, are applied for home range estimation. First, CHPs are applied to simulated animal locational data conforming to five point pattern shapes at three sample sizes. Then, the method is applied to black-footed albatross (Phoebastria nigripes) locational data for illustration and comparison to other methods. For the simulated data, 95% CHPs produced unbiased home range estimates in terms of size for linear and disjoint point patterns and slight underestimates (8-20%) for perforated, concave, and convex ones. The estimated and known home ranges intersected one another by 72-96%, depending on shape and sample size, suggesting that the method has potential as a home range estimator. Additionally, the CHPs applied to estimate albatross home ranges illustrate how the method produces reasonable estimates for bird species that intensively forage in disjoint habitat patches.

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“…Studies that describe biological processes at the local, regional, and global scale increasingly employ thematic information extracted from remotely sensed satellite image data of the earth (Sampson & Delgiudice, 2006). Information from satellite imagery has been applied to diverse contexts, from the quantifi cation of landscape change (Lunetta et al, 2006) to the assessment of alternative energy viability (Dudhani et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Habitat Classifi cation Using Landsat 7ETM+ Imagery of the Ikh Nart Nature Reserve and Surrounding Areas in Dornogobi and Dundgobi Aimags, Mongolia

2006

Mong. J. Biol. Sci.

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Wildlife studies increasingly employ thematic information, such as habitat classes, extracted from remotely sensed image data. Thematic information allows researchers to characterize biological processes of interest at various spatial scales. In and around Ikh Nart Nature Reserve of Mongolia, few large-scale habitat and landscape maps exist. Such maps are needed to support several long-term biological studies. Here we report the results of a maximum likelihood supervised classifi cation of a fi ve-band multispectral composite Landsat 7ETM+ image of the Dalanjargalan Soum section of the reserve and surrounding areas in Dornogobi and Dundgobi Aimags. We classifi ed the image into seven habitat classes: dense rock, low-density shrub, high-density shrub-rock mix, semi-shrub steppe, forbdominated short grass steppe, tall vegetation, and ephemeral standing water bodies. Overall classifi cation accuracy was 90.5% with a K hat statistic of 88.8%, and a user's accuracy of >85% per class, suggesting strong agreement between map and ground reference information. As such, the map presents a detailed distribution of habitats that may be suitable for a variety of research applications including the analysis of wildlife ranging behavior and the identifi cation of priority areas for conservation.

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Tracking the Rapid Pace of GIS-Related Capabilities and Their Accessibility

Cited by 6 publications

References 16 publications

A data management system for long‐term natural resource monitoring and management projects with multiple cooperators

A data management system for long‐term natural resource monitoring and management projects with multiple cooperators

A Characteristic‐Hull Based Method for Home Range Estimation

Habitat Classifi cation Using Landsat 7ETM+ Imagery of the Ikh Nart Nature Reserve and Surrounding Areas in Dornogobi and Dundgobi Aimags, Mongolia

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