A monitoring protocol for vegetation change on Irish peatland and heath |
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| Institution: | 1. West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran;2. Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran;3. School of the Built Environment, Oxford Brookes University, Oxford, UK;4. Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary;5. Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, USA;6. Soil Conservation and Watershed Management Research Institute (SCWMRI), AREEO, Tehran, Iran;7. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;8. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;1. Department of Chemical Engineering, Tunceli University, Tunceli 62000, Turkey;2. Yale-NUIST Center on Atmospheric Environment, Nanjing University of Information, Science and Technology, Nanjing, China;3. School of Forestry and Environmental Studies, Yale University, New Haven, CT, United States;4. Department of Environmental Engineering, Abant Izzet Baysal University, Bolu 14280, Turkey |
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| Abstract: | Amendments to Articles 3.3 and 3.4 of the Kyoto Protocol have meant that detection of vegetation change may now form an interracial part of national soil carbon stocks. In this study multispectral multi-platform satellite data was processed to detect change to the surface vegetation of four peatland sites and one heath in Ireland. Spectral and spatial thresholds were used on difference images between master and slave data in the extraction of temporally invariant targets for multi-platform cross calibration. The Kolmogorov–Smirnov test was used to evaluate any difference in the cumulative probability distributions of the master, slave and calibrated slave data as expressed by the D statistic, with values reduced by an average of 89.7% due to the cross calibration procedure. A change detection model was created which incorporated a spatial threshold of 9 pixels and a standard deviation (SD) spectral threshold. Kappa accuracy values for the five sites ranged from 80 to 97%, showing that 1.5 SD was the optimum spectral threshold for detecting vegetation change. Change detection results showed mean percentage change ranging from 2.11 to 3.28% of total area and cumulative change over the observed time period of between 15.24 and 49.27% of total area. |
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| Keywords: | Change detection Cross calibration Peatlands EVI2 Heaths |
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