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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

False colour composite Landsat MSS image from near-infrared (0.8–1.1 μm) bands of three images (1977, 1980 and 1985), highlighting fires in the Murray-Sunset National Park between 1977 to 1980 (red) and 1980 to 1985 (yellow).Older fire scars are also apparent in shades of blue, grey and green.
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pone.0150808.g001: False colour composite Landsat MSS image from near-infrared (0.8–1.1 μm) bands of three images (1977, 1980 and 1985), highlighting fires in the Murray-Sunset National Park between 1977 to 1980 (red) and 1980 to 1985 (yellow).Older fire scars are also apparent in shades of blue, grey and green.

Mentions: The modelling procedure builds on earlier analyses, including fire history mapping of the region [40] and predictive models of stem age based on stem diameter measurements [37]. Fire mapping was conducted using manual interpretation of Landsat satellite imagery, from 1972 to 2007 [40], plus additional mapping of fires from 2007 to 2011 undertaken using the same methods. Fig 1 shows how fires were highlighted by using a false colour composite image created from three Landsat near-infra red bands. In this example, band 7 of a 1977 and a 1980 image and band 4 of a 1985 image (0.8–1.1 μm) are used to create a false colour composite image. The fires observed in these images occurred in the same location as earlier more-generalised maps of fires in this location. The effect of wind direction on the fire front is apparent, and it is clear that this large scale change in the vegetation is due to fire. Using fire maps created from this technique, we found that 60% of mallee vegetation in the study area had not burnt between 1972 and 2011 [40].


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)

False colour composite Landsat MSS image from near-infrared (0.8–1.1 μm) bands of three images (1977, 1980 and 1985), highlighting fires in the Murray-Sunset National Park between 1977 to 1980 (red) and 1980 to 1985 (yellow).Older fire scars are also apparent in shades of blue, grey and green.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0150808.g001: False colour composite Landsat MSS image from near-infrared (0.8–1.1 μm) bands of three images (1977, 1980 and 1985), highlighting fires in the Murray-Sunset National Park between 1977 to 1980 (red) and 1980 to 1985 (yellow).Older fire scars are also apparent in shades of blue, grey and green.
Mentions: The modelling procedure builds on earlier analyses, including fire history mapping of the region [40] and predictive models of stem age based on stem diameter measurements [37]. Fire mapping was conducted using manual interpretation of Landsat satellite imagery, from 1972 to 2007 [40], plus additional mapping of fires from 2007 to 2011 undertaken using the same methods. Fig 1 shows how fires were highlighted by using a false colour composite image created from three Landsat near-infra red bands. In this example, band 7 of a 1977 and a 1980 image and band 4 of a 1985 image (0.8–1.1 μm) are used to create a false colour composite image. The fires observed in these images occurred in the same location as earlier more-generalised maps of fires in this location. The effect of wind direction on the fire front is apparent, and it is clear that this large scale change in the vegetation is due to fire. Using fire maps created from this technique, we found that 60% of mallee vegetation in the study area had not burnt between 1972 and 2011 [40].

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