Mapping trees in high resolution imagery across large areas using locally variable thresholds guided by medium resolution tree maps |
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| Institution: | 1. Texas A&M University, Department of Ecosystem Science and Management, 534 John Kimbrough Blvd, WFES Building, Room 360, College Station, TX 77843, USA;2. Texas A&M University, Department of Ecosystem Science and Management, 534 John Kimbrough Blvd, WFES Building, Room 334, College Station, TX 77843, USA;3. Texas A&M University, Department of Ecosystem Science and Management, 534 John Kimbrough Blvd, WFES Building, Room 328, College Station, TX 77843, USA;4. Texas A&M University, Department of Ecosystem Science and Management, 474 Olsen Blvd, Kleberg Center, Room 318, College Station, TX 77843, USA;5. University of Florida, School of Forest Resources and Conservation, PO Box 1100410, Gainesville, FL 32611, USA;6. Texas A&M University, Department of Ecosystem Science and Management, 459 Horticulture Road, HFSB Building, Room 302B, College Station, TX 77843, USA;1. Laboratory of Forest Remote Sensing, Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, GR 68200, Orestiada, Greece;2. Laboratory of Forest Management and Remote Sensing, Department of Forestry and Natural Environment, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece;3. Department of Cadastre, Photogrammetry and Cartography, Faculty of Rural and Surveying Engineering, Aristotle University of Thessaloniki, GR-54124, Thessaloniki, Greece |
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| Abstract: | Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists. |
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| Keywords: | Tree cover ADS SPOT5 Landsat |
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