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

Callister KE, Griffioen PA, Avitabile SC, Haslem A, Kelly LT, Kenny SA, Nimmo DG, Farnsworth LM, Taylor RS, Watson SJ, Bennett AF, Clarke MF - PLoS ONE (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia.

ABSTRACT
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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Related in: MedlinePlus

Map of the distribution of age-classes (attributed to fire) of mallee vegetation in the Murray Mallee region as predicted from the artificial neural network model.
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pone.0150808.g003: Map of the distribution of age-classes (attributed to fire) of mallee vegetation in the Murray Mallee region as predicted from the artificial neural network model.

Mentions: Vegetation age was grouped into decades for mapping (Fig 3), with all ages over 90 years grouped into a single category (pre-1920). The oldest site mapped was predicted to be 133 years since fire; however, as Fig 3 illustrates, beyond 85 years, there was an increasing departure from the 1:1 line of perfect fit and under-estimation of age.


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

Callister KE, Griffioen PA, Avitabile SC, Haslem A, Kelly LT, Kenny SA, Nimmo DG, Farnsworth LM, Taylor RS, Watson SJ, Bennett AF, Clarke MF - PLoS ONE (2016)

Map of the distribution of age-classes (attributed to fire) of mallee vegetation in the Murray Mallee region as predicted from the artificial neural network model.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814043&req=5

pone.0150808.g003: Map of the distribution of age-classes (attributed to fire) of mallee vegetation in the Murray Mallee region as predicted from the artificial neural network model.
Mentions: Vegetation age was grouped into decades for mapping (Fig 3), with all ages over 90 years grouped into a single category (pre-1920). The oldest site mapped was predicted to be 133 years since fire; however, as Fig 3 illustrates, beyond 85 years, there was an increasing departure from the 1:1 line of perfect fit and under-estimation of age.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology, Environment and Evolution, La Trobe University, Bundoora, Victoria, Australia.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus