Classifying environmentally significant urban land uses with satellite imagery

Park, Mi-Hyun; Stenstrom, Michael K.

doi:10.1016/j.jenvman.2006.12.010

Cited by 31 publications

(18 citation statements)

References 25 publications

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“…The BNs have been widely used to study land use classification and land use change (e.g. [32,31,5,22,33,23]). The BNs essentially are a probabilistic graphical model that represents the integration of conditional probability functions corresponding to a series of nodes into a joint probability distribution function [3].…”

Section: Methods Evaluationmentioning

confidence: 99%

Integrating spatial relations into case-based reasoning to solve geographic problems

Liang

Sun

2012

Knowledge-Based Systems

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Section: Methods Evaluationmentioning

confidence: 99%

Integrating spatial relations into case-based reasoning to solve geographic problems

Liang

Sun

2012

Knowledge-Based Systems

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“…These nodes may be at different temporal and spatial scales and the data represented in the BN may originate from diverse sources such as empirical data, expert opinion and simulation outputs (Saddo et al, 2005;Borsuk et al, 2006;McCann et al, 2006;Jensen and Nielsen, 2007;Pollino et al, 2007;Park and Stenstrom, 2008). In the case of the Lyngbya network, the outcome of interest is the initiation of a Lyngbya bloom.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

“…The probability table is determined by these states and the states of the nodes that influence it. An example is the conditional probability table (CPT) for the Bottom Current Climate node, shown in Table 1, which has two states (Low and High) and has three parent nodes that influence it (Wind Direction, Wind Speed and Tide) (Saddo et al, 2005;Pollino et al, 2007;Park and Stenstrom, 2008).…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

“…It mandates some statistical knowledge as well as some familiarity with stochastic process theory and Monte Carlo simulation techniques (Goss and Peccoud, 1998). A Bayesian Network (BN) provides a graphical representation of key factors, which are represented as nodes in the diagram and their causal relationships with each other and with the outcome of interest (Borsuk et al, 2006;McCann et al, 2006;Jensen and Nielsen, 2007;Uusitalo, 2007) are depicted as directed links or arrows connecting a 'parent node' to a 'child node', resulting in a directed acyclic graph (DAG) (Saddo et al, 2005;Jensen and Nielsen, 2007;Uusitalo, 2007;Park and Stenstrom, 2008). BNs are better able to portray the complexity of the decision process and the many inter-dependencies between the factors of the decision process (Janssens et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

Johnson

Fielding

Hamilton

et al. 2010

Marine Environmental Research

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Blooms of the cyanobacteria Lyngbya majuscula have occurred for decades around the world. However, with the increase in size and frequency of these blooms, coupled with the toxicity of such algae and their increased biomass, they have become substantial environmental and health issues. It is therefore imperative to develop a better understanding of the scientific and management factors impacting on Lyngbya bloom initiation. This paper suggests an Integrated Bayesian Network (IBN) approach that facilitates the merger of the research being conducted by various parties on Lyngbya. Pivotal to this approach are two Bayesian networks modelling the management and scientific factors of bloom initiation. The research found that Bayesian Networks (BN) and specifically Object Oriented BNs (OOBN) and Dynamic OOBNs facilitate an integrated approach to modelling ecological issues of concern. The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multi-faceted environmental problems.

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“…To support the regulation for more effective and efficient natural resource utilization through the conservation of vegetation-based energy there are various approaches, one of which is spatial approach. It is viable to implement spatial approach by utilizing spatial data in the form of satellite image data which is feasible for analyzing land cover conditions [17,18,19] and land use of an area [20,21,22]. An example of this would be analyzing land use and land cover using satellite imagery as is illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Analysis, Using Landsat 7Etm+ and 8 OLI for Supporting Energy Conservation Policy Based Vegetation in DIY

Fauzy

Putra

Musyarof

et al. 2018

J. Phys.: Conf. Ser.

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Abstract.Energy conservation policy is one of the fundamental bases to take into account in the attempt of natural resources preservation nowadays. It is primarily underpinned by excessive resource exploration and uncontrolled development leading to disturbance of natural balance. In order to support the regulation of natural resource utilization there are various approaches to take into account, one of which is by using spatial approach. Thus, the purpose of this study is to conduct an analysis using remote sensing methods to support a vegetationbased energy conservation policy in the Special Region of Yogyakarta (DIY). On the basis of the research, it is noteworthy that the most rapidly increasing rate of energy utilization is by the general public and business actors. This is as clearly proven by an increase in the number of transportation modes of 2119575 vehicles, an increase of 6.03 % in 2017. Projected increase in the amount of fuel utilization Oil for industrial sector activities amounted to 455975425 liters or 95.26 %. The size of Green Open Space (RTH) land in recent years has not changed significantly. By using forecasting method, the projection of Green Open Space (RTH) is estimated to be 1,090.377 Ha increasing by 12.8 % in 2017. Furthermore, remote sensing analysis using Landsat 7 ETM+ and 8 OLI data indicates that land use in Yogyakarta also undergoes significant changes.

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Classifying environmentally significant urban land uses with satellite imagery

Cited by 31 publications

References 25 publications

Integrating spatial relations into case-based reasoning to solve geographic problems

Integrating spatial relations into case-based reasoning to solve geographic problems

An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiation

Remote Sensing Analysis, Using Landsat 7Etm+ and 8 OLI for Supporting Energy Conservation Policy Based Vegetation in DIY

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