Developing spatially and thematically detailed backdated maps for land cover studies
Global or regional land cover change on a decadal time scale can be studied at a high level of detail using the availability of remote sensing data such as that provided by Landsat. However, there are three main technical challenges in this goal. First, the generation of land cover maps without refe...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:309088 |
| Acceso en línea: | https://ddd.uab.cat/record/309088 https://dx.doi.org/urn:doi:10.1080/17538947.2016.1213320 |
| Access Level: | acceso abierto |
| Palabra clave: | Iberian Peninsula Land cover mapping Landsat Backdating Statistical classifiers |
| Sumario: | Global or regional land cover change on a decadal time scale can be studied at a high level of detail using the availability of remote sensing data such as that provided by Landsat. However, there are three main technical challenges in this goal. First, the generation of land cover maps without reference data is problematic (backdating). Second, it is important to maintain high accuracies in land cover change map products, requiring a reasonably rich legend within each map. Third, a high level of automation is necessary to aid the management of large volumes of data. This paper describes a robust methodology for processing time series of satellite data over large spatial areas. The methodology includes a retrospective analysis used for the generation of training and test data for historical periods lacking reference information. This methodology was developed in the context of research on global change in the Iberian Peninsula. In this study we selected two scenes covering geographic regions that are representative of the Iberian Peninsula. For each scene, we present the results of two classifications (1985-1989 and 2000-2004 quinquennia), each with a legend of 13 categories. An overall accuracy of over 92% was obtained for all 4 maps. |
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