Global Forest Classification Using Jers And Tandem Ers Data
One of the main objectives of the remote sensing, and the scientific community in general, is the development of models and algorithms that are applicable at the global scale. This is especially true for improving methods of forest inventory for carbon accounting. A forest classification scheme base...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.20.4982 http://earth.esa.int/pub/ESA_DOC/gothenburg/334luckm.pdf |
| Summary: | One of the main objectives of the remote sensing, and the scientific community in general, is the development of models and algorithms that are applicable at the global scale. This is especially true for improving methods of forest inventory for carbon accounting. A forest classification scheme based entirely on satellite SAR data, developed for a large area of Siberian Taiga forest and tested using Russian forestry service ground data is applied to different forested systems elsewhere in the world. The scheme is based on tandem ERS coherence and JERS backscatter and stratifies the forest into 6 classes including three timber volume classes. Thirty-five test sites in Siberia, each between 20,000 and 100,000 ha in size, are used to develop the classification algorithm and a further 12 Siberian sites were used in its validation. After accuracy assessment the algorithm was applied to tropical and managed, temperate forest test sites. The results, quantified using kappa statistics, for forests very different in structure to Russian Boreal Forests are surprisingly good although differences in the quality of ground truth make comparisons difficult. |
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