<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. the classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBridge (RapidEye) high resolution satellite imagery, although for distinguishing particular LULC classes, additional satellite images (LANDSAT TM5) and GIS-vector data were used. Accuracy assessment of GEOBIA classification results varied from 0.83 to 0.87 (kappa), depending on the specific catchment area. The main recognized advantages of GEOBIA in the case study were: performing of multi-stage and multi-scale image classification using different features for specific LULC classes and the ability to using knowledge-based classification in conjunction with the data fusion approach in an efficient and reliable manner.</p></abstract>

Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline the expensive mapping process. The aim of the study was to create LULC maps of three selected water catchment areas in South Poland using a Geographic Object-Based Image Analysis (GEOBIA) in order to highlight the advantages of this innovative, semi-automatic method of image analysis. the classification workflow included: multi-stage and multi-scale analyses based on a data fusion approach. Input data consisted mainly of BlackBridge (RapidEye) high resolution satellite imagery, although for distinguishing particular LULC classes, additional satellite images (LANDSAT TM5) and GIS-vector data were used. Accuracy assessment of GEOBIA classification results varied from 0.83 to 0.87 (kappa), depending on the specific catchment area. The main recognized advantages of GEOBIA in the case study were: performing of multi-stage and multi-scale image classification using different features for specific LULC classes and the ability to using knowledge-based classification in conjunction with the data fusion approach in an efficient and reliable manner.

Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

Institute of Forest Resources Management, University of Agriculture in Kraków, Poland

ProGea Consulting, Faculty of Forestry Kraków, Poland

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping"}

Land Use and Land Cover (LULC) maps play an important role in an environmental modelling, and for many years efforts have been made to improve and streamline...

Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

Publié en ligne: 31 mars 2016

Pages: 93 - 104

Reçu: 26 nov. 2015

DOI: https://doi.org/10.1515/quageo-2016-0009

Mots clés
classification, hydrology, OBIA, rapideye, SaLMaR

© Faculty of Geographical and Geological Sciences, Adam Mickiewicz University

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Using Geobia and Data Fusion Approach for Land use and Land Cover Mapping

Publié en ligne: 31 mars 2016

Pages: 93 - 104

Reçu: 26 nov. 2015

DOI: https://doi.org/10.1515/quageo-2016-0009

Mots clésclassification, hydrology, OBIA, rapideye, SaLMaR

© Faculty of Geographical and Geological Sciences, Adam Mickiewicz University

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
classification, hydrology, OBIA, rapideye, SaLMaR