Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region
AuthorCallister, KE; Griffioen, PA; Avitabile, SC; Haslem, A; Kelly, LT; Kenny, SA; Nimmo, DG; Farnsworth, LM; Taylor, RS; Watson, SJ; ...
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
University of Melbourne Author/sKelly, Luke
AffiliationSchool of Ecosystem and Forest Sciences
Document TypeJournal Article
CitationsCallister, K. E., Griffioen, P. A., Avitabile, S. C., Haslem, A., Kelly, L. T., Kenny, S. A., Nimmo, D. G., Farnsworth, L. M., Taylor, R. S., Watson, S. J., Bennett, A. F. & Clarke, M. F. (2016). Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region. PLOS ONE, 11 (3), https://doi.org/10.1371/journal.pone.0150808.
Access StatusOpen Access
Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km(2) study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery.
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