Abstract
In this chapter forest cover and forest cover change mapping basing on image objects is discussed. Change relates to recent complete harvesting and reestablishment, degradation or thinning is not considered. For the change maps two different strategies are proposed. The first one derives the changes by means of previously classified images of a multitemporal dataset and is thus referred to as “post-classification change detection”. For increasing the accuracy of the change maps a knowledge based change detection approach is introduced. The second strategy considers all scenes of the multitemporal dataset simultaneously. This method is referred to as “multidate classification”.
Generally any kind of Earth Observation (EO) data allowing the grey value based separation of forest and non-forest can be applied with both strategies. In this study, JERS-1 (Japanese Earth Resource Satellite) SAR data are used for method development. The feasibility assessment of both object based mapping strategies is performed at five test sites: Germany (Thuringia), UK (Kielder), Sweden (Remningstorp and Brattåker) and Russia (Chunsky). Due to the specific data requirements (broad multitemporal dataset) the first approach could only be successfully implemented at the Thuringia site. It was also tested at Kielder, but with deficient results.
The chapter concludes with the successful realisation of the approach at the Russian service case of GSE FM. Because of the given time frame (1990-recent) other EO data sources had to be implemented. As historical EO data source LANDSAT TM was selected, recent information is derived from ASAR APP.
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Thiel, C., Thiel, C., Riedel, T., Schmullius, C. (2008). Object based classification of SAR data for the delineation of forest cover maps and the detection of deforestation – A viable procedure and its application in GSE Forest Monitoring. In: Blaschke, T., Lang, S., Hay, G.J. (eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77058-9_18
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