Handling heterogeneous bipolar information for modelling environmental syndromes of global change

Bordogna, Gloria; Boschetti, Mirco; Brivio, Pietro Alessandro; Carrara, Paola; Stroppiana, Daniela; Weissteiner, Christof J.

doi:10.1016/j.envsoft.2012.03.016

Cited by 6 publications

(2 citation statements)

References 45 publications

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“…The fusion process was designed because the reliability of the two indices needs to be adjustable to accommodate intricate land cover patterns. Indices may be imperfect; therefore, the expert should consider their imperfections, thereby specifying a partial trust [42]. In the fusion process, indices with partial trust are weighted proportionally to their trust in the result.…”

Section: Fusion Of Entropy-based Indicesmentioning

confidence: 99%

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

Jin

Peng

Zhang

et al. 2020

IJGI

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Recognizing land cover heterogeneity is essential for the assessment of spatial patterns to guide conservation planning. One of the top research priorities is the quantification of land cover heterogeneity using effective landscape metrics. However, due to the diversity of land cover types and their varied distribution, a consistent, larger-scale, and standardized framework for heterogeneity information extraction from this complex perspective is still lacking. Consequently, we developed a new Land Cover Complexity Index (LCCI), which is based on information-theory. The LCCI contains two foundational aspects of heterogeneity, composition and configuration, thereby capturing more comprehensive information on land cover patterns than any single metric approach. In this study, we compare the performance of the LCCI with that of other landscape metrics at two different scales, and the results show that our newly developed indicator more accurately characterizes and distinguishes different land cover patterns. LCCI provides an alternative way to measure the spatial variation of land cover distribution. Classification maps of land cover heterogeneity generated using the LCCI provide valuable insights and implications for regional conservation planning. Thus, the LCCI is shown to be a consistent indicator for the quantification of land cover heterogeneity that functions in an adaptive way by simultaneously considering both composition and configuration.

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Section: Fusion Of Entropy-based Indicesmentioning

confidence: 99%

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

Jin

Peng

Zhang

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

“…The development of highly heterogeneous environmental indicators has been widely investigated, including semantic interpolation and fuzzy similarity approaches; first, second or higher order approximations (Dubois et al, 1997;Mas et al, 2012;Bordogna et al, 2012). Land use modelling is an important approach for evaluating global environmental impact (P erez- .…”

Section: Introductionmentioning

confidence: 99%

Multi-scale land-use disaggregation modelling: Concept and application to EU countries

Lamboni

Koeble

Leip

2016

Environmental Modelling & Software

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a b s t r a c tChanges of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agrienvironmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 Â 10 5 HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75 % of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning.

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Enhancing Remotely Sensed Low Resolution Vegetation Data for Assessing Mediterranean Areas Prone to Land Degradation

Weissteiner

Böttcher

Sommer

2014

Land Use and Land Cover Mapping in Europe

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Handling heterogeneous bipolar information for modelling environmental syndromes of global change

Cited by 6 publications

References 45 publications

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

Index for the Consistent Measurement of Spatial Heterogeneity for Large-Scale Land Cover Datasets

Multi-scale land-use disaggregation modelling: Concept and application to EU countries

Enhancing Remotely Sensed Low Resolution Vegetation Data for Assessing Mediterranean Areas Prone to Land Degradation

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