Feral Pig Control Difficulty Index

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About this Collection

Feral Pig Control Difficulty Index

The control difficulty index (CDI) has been developed to provide a spatial assessment of the estimated difficulty of enacting an African Swine Fever (ASF) suppression program given an outbreak for any region across Australia. This layer is estimated at a spatial grain of 30-arcseconds (approx. 1 km2). The index provides a representation of the esti... more

Environmental Management Invasive Species Ecology

18 Mar 2020

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Sus scrofa feral pig swine


The Feral Pig CDI is derived using the following layers. 1. Friction Index: The estimated time to travel across each 1km pixel given their specific land cover type. See Weiss et al 2018 for details. 2. Accessibility Index. Shortest travel time to the nearest population centre – this input includes roads and tracks among other input layers. Methods following Weiss et al 2018 but customised to suit Australian population centres. 3. Terrain Ruggedness Index: Terrain Ruggedness Index (TRI) Mean value of TRI, using Median Statistic, 7.5 arc-seconds. Aggregation done at 1km (0.00833 degree) by calculating the mean values among 16 pixels. Calculated from the GMTED2010 7.5 arc –second product. Data available from the U.S. Geological Survey (Danielson and Gesch 2011). 4. Canopy visibility: Tree cover (%) - using Hansen Global Foliage Change layer (Hansen et al 2013) Layers are scaled to each range between 0-1 using logistic scaling functions and then summed to create a single layer (see assumptions below). The map is scaled between zero and three with increasing values reflecting changes in the difficulty of the area to complete control operations. Areas which are closer to 0 reflect areas with many roads and tracks, flatter ground, open canopy and close to a city or town. At the other extreme, areas closer to 3 reflect rugged terrain, with closed canopy, limited access and further from built up areas. Each of the values are weighted to create the combined map so dark red areas can also reflect extreme values of one or a combination of the inputs. Assumptions Scaling – each of the input layers is scaled to between 0 and 1 from their native range to ensure appropriate contribution of each layer into the index. We chose to use a logistic scaling function, which scales data via an S shaped relationship following; y= 1/〖1+ e〗^(-α(x-β)) where x is a vector of the original values, i represents the slope or steepness of the S and z gives the point at which the curve crosses 0.5 along the 0-1 scale. We chose this class of function to provide a representation that within any one of these layers, at the low end a range of values is likely to represent low difficulty and conversely the same occurs at the high end of the range for high difficulties. For each layer we scaled them using individual slope and 0.5 crossing values given an expert opinion of how these curves should represent the range of data, and assumed difficulties, present in each layer. These values can be changed, given additional inputs/knowledge to further refine the index. Weighting – When developing the index we weighted the input of each scaled layer differently following the assumption that some activities will weight more heavily in contributing to the difficulty of controlling feral pigs. We up weighted the friction and accessibility layers, using a 2 and 3 times multiplier respectively, following the assumption that mobilising ground based resources to a region and, once there, utilising them inside that region were more difficult than enacting controls that occurred primarily by air. As such the index represents the weighted sum of each of the four layers following; CDI=2*Friction+3*Accessibility+Terrain Ruggedness+Canopy Visibility The use of four layers and the weighting scheme means that the raw CDI can exceed the range of 0-3 in the provided index. We truncated the raw CDI calculations to 3, where all values above 3 are given a value of 3, following the assumption that where the contribution of 3 or more maximum levels of difficulty (as each input range 0-1) occur that these areas should be equally classed a very difficult.

References. Danielson JJ, Gesch DB (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010). U.S. Geo-logical Survey Open-File Report 2011–1073 D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature (2018). doi:10.1038/nature25181 Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from:

CSIRO Data Licence

CSIRO (Australia)

Hoskins, Andrew; Perry, Justin (2020): Feral Pig Control Difficulty Index. v2. CSIRO. Data Collection.

All Rights (including copyright) CSIRO 2020.

The metadata and files (if any) are available to the public.

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Location Details

9°18′0″ S

41°47′60″ S

154°54′0″ E

110°50′49.344″ E


About this Project

NESP NAER wetland mgmt

Biodiversity impacts of feral animals.

Justin Perry

Andrew Hoskins

Justin Perry

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