Accurate mapping of forest types using dense seasonal Landsat time-series |
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| Institution: | 1. College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, PR China;2. Center for Spatial Information Science and Sustainable Development, 1239 Siping Road, Shanghai 200092, PR China;3. Shanghai BaoSteel Industry Technological Service Co., LTD, 3521 Tongji Road, Shanghai 201900, PR China;1. ASRC InuTeq, Contractor to the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA;2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;4. U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198-0001, USA;5. Sigma Space Corporation, VIIRS Characterization Support Team (VCST), Lanham, MD 20706, USA;1. Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;2. Institute for Silviculture, University of Natural Resources and Life Sciences (BOKU) Vienna, Peter-Jordan-Str. 82, 1190 Vienna, Austria;3. Integrative Research Institute on Transformation of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany;1. Department of Earth and Environmental Sciences, Division Forest, Nature and Landscape Research, Katholieke Universiteit Leuven, Celestijnenlaan 200E – Bus 2411, B-3001 Leuven, Belgium;2. Flemish Institute for Technological Research (VITO), Centre for Remote Sensing and Earth Observation Processes (TAP), Boeretang 200, BE-2400 Mol, Belgium;3. Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA 94305 , USA |
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| Abstract: | An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales. |
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| Keywords: | Forest types Classification Landsat Seasonal time-series Hierarchical approach Feature selection |
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