Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region.

Autor: Callister KE; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Griffioen PA; Arthur Rylah Institute for Environmental Research, Department of Environment, Land, Water and Planning, Heidelberg, Victoria, Australia., Avitabile SC; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Haslem A; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Kelly LT; School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia., Kenny SA; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Nimmo DG; School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia., Farnsworth LM; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Taylor RS; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Watson SJ; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia., Bennett AF; School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia., Clarke MF; Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2016 Mar 30; Vol. 11 (3), pp. e0150808. Date of Electronic Publication: 2016 Mar 30 (Print Publication: 2016).
DOI: 10.1371/journal.pone.0150808
Abstrakt: 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.
Databáze: MEDLINE
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