Modeling Sage Grouse: Progressive Computational Methods for Linking a Complex Set of Local, Digital Biodiversity and Habitat Data Towards Global Conservation Statements and Decision-Making Systems

Onyeahialam, Anthonia; Huettmann, Falk; Bertazzon, Stefania

doi:10.1007/11424857_17

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The process of the Markov model is characterized as memorylessness. This means that in a time series, the probability distribution in the next state is only dependent on the current state, not on previous states [42,43]. The Markov model can be used with different statistical or artificial intelligent models to predict land use/cover changes, such as the cellular automata model, logistic model, multilayer perception/neural networks, and hybrid models [44][45][46].…”

Section: Computation Of Transition Area Matrix Using the Markov Modelmentioning

confidence: 99%

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

2018

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Rapid urbanization is occurring throughout China, especially in megacities. Using a land use model to obtain future land use/cover conditions is an essential method to prevent chaotic urban sprawl and imbalanced development. This study utilized historical Landsat images to create land use/cover maps to predict the land use/cover changes of Tianjin city in 2025 and 2035. The cellular automata–Markov (CA–Markov) model was applied in the simulation under three scenarios: the environmental protection scenario (EPS), crop protection scenario (CPS), and spontaneous scenario (SS). The model achieved a kappa value of 86.6% with a figure of merit (FoM) of 12.18% when compared to the empirical land use/cover map in 2015. The results showed that the occupation of built-up areas increased from 29.13% in 2015 to 38.68% (EPS), 36.18% (CPS), and 47.94% (SS) in 2035. In this context, current urbanization would bring unprecedented stress on agricultural resources and forest ecosystems, which could be attenuated by implementing protection policies along with decelerating urban expansion. The findings provide valuable information for urban planners to achieve sustainable development goals.

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Section: Computation Of Transition Area Matrix Using the Markov Modelmentioning

confidence: 99%

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

2018

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“…However, the primary goal was to identify the significance of various human and environmental factors related to Loggerhead nesting. Future research could develop forecasting models, using techniques such as Markov chains, where hindcasting and forecasting future nesting counts could be implemented [42][43][44].…”

Section: Recommendationsmentioning

confidence: 99%

Nesting Patterns of Loggerhead Sea Turtles (Caretta caretta): Development of a Multiple Regression Model Tested in North Carolina, USA

Halls

Randall

2018

IJGI

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Numerous environmental conditions may influence when a female Loggerhead sea turtle (Caretta caretta) selects a nesting site. Limited research has used Geographic Information Systems (GIS) and statistical analysis to study sea turtle spatial patterns and temporal trends. Therefore, the goals of this research were to identify areas that were most prevalent for nesting and to test social and environmental variables to create a nesting suitability predictive model. Data were analyzed at all barrier island beaches in North Carolina, USA (515 km) and several variables were statistically significant: distance to hardened structures, beach nourishment, house density, distance to inlets, and beach elevation, slope, and width. Interestingly, variables that were not significant were population density, proximity to the Gulf Stream, and beach aspect. Several statistical techniques were tested and Negative Binomial Distribution produced good regional results while Geographically Weighted Regression models successfully predicted the number of nests with an average of 75% of the variance explained. Therefore, the combination of traditional and spatial statistics provided insightful predictive modeling results that may be incorporated into management strategies and may have important implications for the designation of critical Loggerhead nesting habitats.

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“…Furthermore, changes in habitat selection from the past to the present can be predicted by extrapolating the model to past conditions. The information used to evaluate such change can be very useful when considering the scale of human-wildlife conflicts (Warren et al, 2001;Onyeahialam et al, 2005;McDermid et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Change in habitat selection by Japanese macaques (Macaca fuscata) and habitat fragmentation analysis using temporal remotely sensed data in Niigata Prefecture, Japan

Mochizuki¹,

Murakami²

2011

International Journal of Applied Earth Observation and Geoinfor

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Modeling Sage Grouse: Progressive Computational Methods for Linking a Complex Set of Local, Digital Biodiversity and Habitat Data Towards Global Conservation Statements and Decision-Making Systems

Cited by 7 publications

References 22 publications

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Scenario-Based Simulation of Tianjin City Using a Cellular Automata–Markov Model

Nesting Patterns of Loggerhead Sea Turtles (Caretta caretta): Development of a Multiple Regression Model Tested in North Carolina, USA

Change in habitat selection by Japanese macaques (Macaca fuscata) and habitat fragmentation analysis using temporal remotely sensed data in Niigata Prefecture, Japan

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