Remote Sens., Volume 10, Issue 9 (September 2018) – 180 articles
Cover Story (view full-size image): The analysis and classification of tree species and tree species groups has a long history in remote sensing and is not only of scientific interest but also important for applied forestry. Different machine-learning algorithms (Random Forest and Support Vector Machines) are tested using Sentinel-2 data and forest inventory data in object- and pixel-based approaches to classify tree species in two German forest areas. The proposed semi-automatic and highly scalable workflow achieved an overall accuracy of 88%, indicating that it is well-suited to be used on larger areas with a similar forest structure. Moreover, the proposed method allows a streamlined workflow for applied forestry by providing analysis results directly to mobile applications for validation and data collection in the field. View this paper.
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