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Bennett, Brandon

doi:10.1023/a:1017965025666

Cited by 103 publications

(6 citation statements)

References 12 publications

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“…It essentially serves to depict and report complex spatial patterns and also produce simplified illustration. The concept of land cover classes, however, is inherently vague, and this vagueness tends to persist even when steps are taken to precisely define those land cover classes (Bennett 2001). Defining forest land is such a problem and has been discussed for decades.…”

Section: Discussionmentioning

confidence: 99%

Uncertainties of forest area estimates caused by the minimum crown cover criterion

Magdon

Kleinn

2012

Environ Monit Assess

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Defining “forest land” is a complex issue and has been discussed for decades. Today, a confusing multitude of definitions of forest land are in use making comparison of forest area figures difficult. But currently, comparability is receiving much attention when it comes to install market mechanisms for ecosystem services. Minimum crown cover is among the most frequently employed criteria of forest definitions. However, the size of the reference area on which the crown cover percent is to be measured is usually not defined. But how does a change of the size of the reference area affect the derived forest cover? In this study, we analyze the interactions of the crown cover threshold and the size of the reference area. We start with analyzing the interactions using a simple geometric model of the forest edge. Then, we extend the analysis by simulating artificial landscapes where we study how the interaction is affected by different degrees of forest fragmentation, crown cover proportion, and spatial resolution of the data source used. The simulation showed that large differences in forest cover (>50 %) may result for a fixed crown cover threshold value, just by changing the size of the reference area, where the magnitude of this effect is a function of the chosen threshold value and the spatial configuration of the crowns. As a consequence of the findings, we see an urgent need to complete forest definitions by defining a reference area in order to reduce uncertainties of forest cover estimates.

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Section: Discussionmentioning

confidence: 99%

Uncertainties of forest area estimates caused by the minimum crown cover criterion

Magdon

Kleinn

2012

Environ Monit Assess

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“…This high density criterion also means that no two neighbouring trees should be too far apart. This close clustering of trees satisfies the maximal connectedness requirement, which is one of the most important factors in determining whether a component belongs to a vague region [11]. Finally, the spatial distribution of these trees should follow an areal pattern.…”

Section: Modeling Vague Regions From An Ontological Perspectivementioning

confidence: 77%

“…because "downtown" itself is an ontologically vague concept [2]. Bennett (2001) proposed a similar question, "What is a forest?" In addition, there is an ontological distinction between natural/bona fide boundaries and artificial/fiat boundaries: the former can be conceptualized based on the physical discontinuity or qualitative heterogeneity between an entity (such as a soccer field) and its surroundings, whereas the latter is defined more subjectively (e.g., a downtown area) [4,36].…”

Section: Modeling Vague Regions From An Ontological Perspectivementioning

confidence: 99%

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Modeling the Vagueness of Areal Geographic Objects: A Categorization System

Liu

Yuan

Gao

2019

IJGI

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Modeling vague objects with indeterminate boundaries has drawn much attention in geographic information science. Because fields and objects are two perspectives in modeling geographic phenomena, this paper investigates the characteristics of vague regions from the perspective of the field/object dichotomy. Based on the assumption that a vague object can be viewed as the conceptualization of a field, we defined five categories of vague objects: direct field-cutting objects, focal operation-based field-cutting objects, element-clustering objects, object-referenced objects, and dynamic boundary objects. We then established a categorization system to formalize the semantic differences between vague objects using the fuzzy set theory. The proposed framework provides valuable input for the conceptualization, interpretation, and modeling of vague geographical objects.

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“…The first issue concerns the definition of the classes of interest. Indeed, geographic concepts can have many definitions (Bennett 2002) depending on the physical, historical, functional or conventional mode of definition used (Bennett 2005) (Figure 3, 1 -Modes of definitions). Figure 1.…”

Section: The Complex Definition Of Geographic Conceptsmentioning

confidence: 99%

Ontologies to interpret remote sensing images: why do we need them?

Arvor

Belgiu

Falomir

et al. 2019

GIScience & Remote Sensing

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The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts' expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.

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Untitled

Cited by 103 publications

References 12 publications

Uncertainties of forest area estimates caused by the minimum crown cover criterion

Uncertainties of forest area estimates caused by the minimum crown cover criterion

Modeling the Vagueness of Areal Geographic Objects: A Categorization System

Ontologies to interpret remote sensing images: why do we need them?

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