Functional classification of spatially heterogeneous environments: the Land Cover Mosaic approach in remote sensing

M.H. Obbink

Research output: Thesisinternal PhD, WU

Abstract

Tropical rainforest areas are difficult to classify in the digital analysis of remote sensing data because of spatial heterogeneity. Often many technical solutions are adopted to reduce the ‘problem’ of spatial heterogeneity. This thesis describes theory and methods that now use this heterogeneity during the digital image classification. With spatial heterogeneity, spatial aggregation levels such as patches,patch-mosaics and landscapes can be distinguished. Consequently, vegetation patterns can be related to functional management units at different decision-levels. The developed theory and methods thus save two birds with one stone: (a) the classification is completely digitally, and (b) the classification provides information on deforestation that meets the needs of decision-makers. This thesis further recommends approaching all land cover classifications from a heterogeneous perspective for understanding and controlling environmental processes on a global level. This can enhance a sustainable development of tropical rainforest areas for the benefit of future generations.

  

Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Wageningen University
Supervisors/Advisors
  • Molenaar, M., Promotor
  • Clevers, Jan, Co-promotor
Award date6 Sep 2011
Place of Publication[S.l.]
Publisher
Print ISBNs9789085859956
Publication statusPublished - 2011

Fingerprint

land cover
remote sensing
rainforest
image classification
digital image
deforestation
sustainable development
mosaic
vegetation
thesis
decision
method

Keywords

  • remote sensing
  • heterogeneity
  • tropical rain forests
  • spatial variation
  • classification
  • landscape ecology
  • decision making

Cite this

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title = "Functional classification of spatially heterogeneous environments: the Land Cover Mosaic approach in remote sensing",
abstract = "Tropical rainforest areas are difficult to classify in the digital analysis of remote sensing data because of spatial heterogeneity. Often many technical solutions are adopted to reduce the ‘problem’ of spatial heterogeneity. This thesis describes theory and methods that now use this heterogeneity during the digital image classification. With spatial heterogeneity, spatial aggregation levels such as patches,patch-mosaics and landscapes can be distinguished. Consequently, vegetation patterns can be related to functional management units at different decision-levels. The developed theory and methods thus save two birds with one stone: (a) the classification is completely digitally, and (b) the classification provides information on deforestation that meets the needs of decision-makers. This thesis further recommends approaching all land cover classifications from a heterogeneous perspective for understanding and controlling environmental processes on a global level. This can enhance a sustainable development of tropical rainforest areas for the benefit of future generations.   ",
keywords = "remote sensing, heterogeniteit, tropische regenbossen, ruimtelijke variatie, classificatie, landschapsecologie, besluitvorming, remote sensing, heterogeneity, tropical rain forests, spatial variation, classification, landscape ecology, decision making",
author = "M.H. Obbink",
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language = "English",
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publisher = "S.n.",
school = "Wageningen University",

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Functional classification of spatially heterogeneous environments: the Land Cover Mosaic approach in remote sensing. / Obbink, M.H.

[S.l.] : S.n., 2011. 304 p.

Research output: Thesisinternal PhD, WU

TY - THES

T1 - Functional classification of spatially heterogeneous environments: the Land Cover Mosaic approach in remote sensing

AU - Obbink, M.H.

N1 - WU thesis no. 5052

PY - 2011

Y1 - 2011

N2 - Tropical rainforest areas are difficult to classify in the digital analysis of remote sensing data because of spatial heterogeneity. Often many technical solutions are adopted to reduce the ‘problem’ of spatial heterogeneity. This thesis describes theory and methods that now use this heterogeneity during the digital image classification. With spatial heterogeneity, spatial aggregation levels such as patches,patch-mosaics and landscapes can be distinguished. Consequently, vegetation patterns can be related to functional management units at different decision-levels. The developed theory and methods thus save two birds with one stone: (a) the classification is completely digitally, and (b) the classification provides information on deforestation that meets the needs of decision-makers. This thesis further recommends approaching all land cover classifications from a heterogeneous perspective for understanding and controlling environmental processes on a global level. This can enhance a sustainable development of tropical rainforest areas for the benefit of future generations.   

AB - Tropical rainforest areas are difficult to classify in the digital analysis of remote sensing data because of spatial heterogeneity. Often many technical solutions are adopted to reduce the ‘problem’ of spatial heterogeneity. This thesis describes theory and methods that now use this heterogeneity during the digital image classification. With spatial heterogeneity, spatial aggregation levels such as patches,patch-mosaics and landscapes can be distinguished. Consequently, vegetation patterns can be related to functional management units at different decision-levels. The developed theory and methods thus save two birds with one stone: (a) the classification is completely digitally, and (b) the classification provides information on deforestation that meets the needs of decision-makers. This thesis further recommends approaching all land cover classifications from a heterogeneous perspective for understanding and controlling environmental processes on a global level. This can enhance a sustainable development of tropical rainforest areas for the benefit of future generations.   

KW - remote sensing

KW - heterogeniteit

KW - tropische regenbossen

KW - ruimtelijke variatie

KW - classificatie

KW - landschapsecologie

KW - besluitvorming

KW - remote sensing

KW - heterogeneity

KW - tropical rain forests

KW - spatial variation

KW - classification

KW - landscape ecology

KW - decision making

M3 - internal PhD, WU

SN - 9789085859956

PB - S.n.

CY - [S.l.]

ER -