Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data

Cole, Beth; Smith, Geoff; Balzter, Heiko

doi:10.1016/j.jag.2018.06.003

Cited by 39 publications

(35 citation statements)

References 39 publications

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“…It is quite surprising, taking into account the fact that terrain accuracy of CORINE is much lower than of BDOT. As a result, CORINE does not include small-scale and dispersed patches of LC, such as settlement or plantings [61,62], which should be more clearly defined [63]. In the research, it is reflected by the fact that the values of NP, PR and MPA differ significantly between two data sources.…”

Section: Methodology Of Lq Analysis Based On the Application Of Remotmentioning

confidence: 99%

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

Sowińska-Świerkosz

Michalik-Śnieżek

2020

Sustainability

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Landscape quality (LQ) encompasses diverse characteristic of the natural and cultural environment. The most effective tool to analyze LQ is the use of indicators. The main problem in the assessment of LQ is not the lack of indicators, but its multitude. That is why, the indicators’ categorization is a problematic issue. The paper aims to introduce and test the methodology for selecting the suitable indicators based on the example of two national parks located in the south-east part of Poland. The method composed of the following stages: (1) Selection of spatial units being analyzed; (2) selection of indicators type(s); (3) selection of specific indicators; (4) calculation of indicator set no 1; (5) analysis of the correlation between indicators’ pairs; (6) selection of a final set of indicators; (7) analysis of effectiveness. The latter stage, refers to the statistical analysis of significance between results obtained dependently on the data sources, a spatial unit of analysis and analyzed regions. The results showed that the categorization composed of ten, mainly composite indicators, can be applied to conclude on different levels of LQ of protected areas. The differences between two analyzed data sources, different spatial units, as well as diverse regions, occurred to be statistically insignificant. Generally, the results of the effectiveness analysis showed that a final categorization of LQ indicators is adequate to conclude on the diverse dimensions of LQ of analyzed protected areas.

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Section: Methodology Of Lq Analysis Based On the Application Of Remotmentioning

confidence: 99%

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

Sowińska-Świerkosz

Michalik-Śnieżek

2020

Sustainability

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“…The CLC contains highly generalized data that may not be suitable for research purposes or for analyzing various areas at a scale of 1:1000 or higher, including city districts, cities, and suburban zones where highly detailed information is needed. The CLC is not appropriate for examining small and fragmented areas because the relevant data are highly generalized [53,54]. However, CLC data are widely available and easy to acquire, and they can be applied in small-scale analyses where a high level of detail is not required.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

Szarek-Iwaniuk

2021

Sustainability

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Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

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“…Remote sensing has been used for decades to monitor land cover change and map the growth of impervious surface. This has led to the generation of important databases at national or global scales, such as the National Land Cover Database (NLCD) in the United States [12,13], Coordination of Information on the Environment (CORNIE) land cover inventory in Europe [14], Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) database in China [15], and MODIS Land Cover Type Yearly Global dataset [16]. As these databases are derived from medium-resolution satellite imagery with pixel size ranging from 30 m to 500 m, they have to use multiple urban land cover classes (i.e., low-and high-intensity developed areas) to reflect different levels of impervious surface cover at the scale of a satellite image pixel.…”

Section: Introductionmentioning

confidence: 99%

Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

2020

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The quantification of impervious surface through remote sensing provides critical information for urban planning and environmental management. The acquisition of quality reference data and the selection of effective predictor variables are two factors that contribute to the low accuracies of impervious surface in urban remote sensing. A hybrid method was developed to improve the extraction of impervious surface from high-resolution aerial imagery. This method integrates ancillary datasets from OpenStreetMap, National Wetland Inventory, and National Cropland Data to generate training and validation samples in a semi-automatic manner, significantly reducing the effort of visual interpretation and manual labeling. Satellite-derived surface reflectance stability is incorporated to improve the separation of impervious surface from other land cover classes. This method was applied to 1-m National Agriculture Imagery Program (NAIP) imagery of three sites with different levels of land development and data availability. Results indicate improved extractions of impervious surface with user’s accuracies ranging from 69% to 90% and producer’s accuracies from 88% to 95%. The results were compared to the 30-m percent impervious surface data of the National Land Cover Database, demonstrating the potential of this method to validate and complement satellite-derived medium-resolution datasets of urban land cover and land use.

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Acceleration and fragmentation of CORINE land cover changes in the United Kingdom from 2006–2012 detected by Copernicus IMAGE2012 satellite data

Cited by 39 publications

References 39 publications

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

The Methodology of Landscape Quality (LQ) Indicators Analysis Based on Remote Sensing Data: Polish National Parks Case Study

A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

Extracting Impervious Surface from Aerial Imagery Using Semi-Automatic Sampling and Spectral Stability

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