Title: ES4LUCC: A GIS-tool for remotely monitoring landscape dynamics
Authors: FORZIERI GIOVANNIBATTISTINI AlessandroCATANI Filippo
Citation: COMPUTERS \& GEOSCIENCES vol. 49 no. 0 p. 72-80
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Publication Year: 2012
JRC N°: JRC77606
ISSN: 0098-3004
URI: http://0-www.sciencedirect.com.precise.petronas.com.my/science/article/pii/S0098300412002117
http://publications.jrc.ec.europa.eu/repository/handle/JRC77606
DOI: 10.1016/j.cageo.2012.06.016
Type: Articles in periodicals and books
Abstract: Given the potential impacts of land cover changes on surface processes, accurate mapping of landscape dynamics is a crucial task in environmental monitoring. The use of commercial software for remote sensing of landscape changes requires appropriate expertise in sensor technology and computing resources that are not always available to decision makers. This paper presents the development of an experimental prototype of a lightweight and user-friendly GIS tool – ES4LUCC – a semiautomatic software for change detection and classification of land use/cover. The tool is based on image processing techniques applied on multi-temporal remotely sensed spectral and surface model data. The GIS-based tiling approach allows to non-specialists of remote sensing to manage high-dimensional data even from low performance computing platforms. The paper synthesizes the implemented digital image processing that form the basis of ES4LUCC, including data correction, classification and change detection, map refinements. It also describes the software architecture, the main IDL modules and the integration with GIS through a tight coupling approach and.dll calling functions. The main modelling process is controlled through a powerful GUI developed as part of the ArcMap component of ESRI ArcGIS. The software is tested by using bi-temporal color-infrared ADS40 and Light detection and ranging data acquired on a 80-km transect of the Marecchia river (Italy). The outputs of ES4LUCC give an understanding of the natural- and human-induced surface processes, such as urban planning, agricultural and forest practices, fluvial dynamics and slope instability. The model provides reliable maps (90.77% overall classification accuracy) that represent useful layers for environmental landscape management.
JRC Directorate:Sustainable Resources

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