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Using State-and-Transition Modeling to Account for Imperfect Detection in Invasive Species Management
- Leonardo Frid, Tracy Holcombe, Jeffrey T. Morisette, Aaryn D. Olsson, Lindy Brigham, Travis M. Bean, Julio L. Betancourt, Katherine Bryan
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- Journal:
- Invasive Plant Science and Management / Volume 6 / Issue 1 / March 2013
- Published online by Cambridge University Press:
- 20 January 2017, pp. 36-47
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- Article
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Buffelgrass, a highly competitive and flammable African bunchgrass, is spreading rapidly across both urban and natural areas in the Sonoran Desert of southern and central Arizona. Damages include increased fire risk, losses in biodiversity, and diminished revenues and quality of life. Feasibility of sustained and successful mitigation will depend heavily on rates of spread, treatment capacity, and cost–benefit analysis. We created a decision support model for the wildland–urban interface north of Tucson, AZ, using a spatial state-and-transition simulation modeling framework, the Tool for Exploratory Landscape Scenario Analyses. We addressed the issues of undetected invasions, identifying potentially suitable habitat and calibrating spread rates, while answering questions about how to allocate resources among inventory, treatment, and maintenance. Inputs to the model include a state-and-transition simulation model to describe the succession and control of buffelgrass, a habitat suitability model, management planning zones, spread vectors, estimated dispersal kernels for buffelgrass, and maps of current distribution. Our spatial simulations showed that without treatment, buffelgrass infestations that started with as little as 80 ha (198 ac) could grow to more than 6,000 ha by the year 2060. In contrast, applying unlimited management resources could limit 2060 infestation levels to approximately 50 ha. The application of sufficient resources toward inventory is important because undetected patches of buffelgrass will tend to grow exponentially. In our simulations, areas affected by buffelgrass may increase substantially over the next 50 yr, but a large, upfront investment in buffelgrass control could reduce the infested area and overall management costs.
14 - Multitemporal and multisensor image registration
- from PART IV - Applications and Operational Systems
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- By Jacqueline Le Moigne, NASA Goddard Space Flight Center, Maryland, Arlene A. Cole-Rhodes, Morgan State University, Maryland, Roger D. Eastman, Loyola University, Maryland, Nathan S. Netanyahu, University of Maryland, Maryland, Harold S. Stone, NEC Research Laboratory Retiree, New Jersey, Ilya Zavorin, University of Maryland, Maryland, Jeffrey T. Morisette, NASA Goddard Space Flight Center, Maryland
- Edited by Jacqueline Le Moigne, NASA-Goddard Space Flight Center, Nathan S. Netanyahu, Roger D. Eastman, Loyola University Maryland
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- Book:
- Image Registration for Remote Sensing
- Published online:
- 03 May 2011
- Print publication:
- 24 March 2011, pp 293-338
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- Chapter
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Summary
Abstract
Registration of multiple source imagery is one of the most important issues when dealing with Earth science remote sensing data where information from multiple sensors exhibiting various resolutions must be integrated. Issues ranging from different sensor geometries, different spectral responses, to various illumination conditions, various seasons and various amounts of noise, need to be dealt with when designing a new image registration algorithm. This chapter represents a first attempt at characterizing a framework that addresses these issues, in which possible choices for the three components of any registration algorithm are validated and combined to provide different registration algorithms. A few of these algorithms were tested on three different types of datasets – synthetic, multitemporal and multispectral. This chapter presents the results of these experiments and introduces a prototype registration toolbox.
Introduction
In Chapter 1, we showed how the analysis of Earth science data for applications, such as the study of global environmental changes, involves the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, spectral, and spatial resolutions. For such applications, the first required step is fast and automatic image registration which can provide precision correction of satellite imagery, band-to-band calibration, and data reduction for ease of transmission. Furthermore, future decision support systems, intelligent sensors and adaptive constellations will rely on real- or near-real-time interpretation of Earth observation data, performed both onboard and at ground-based stations.