Assessing the utility of aerial imagery to quantify the density, age structure and spatial pattern of alien conifer invasions
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Effective detection and monitoring tools are essential to manage the major ecological and economic problems posed by alien conifer invasions. Low-cost aerial imagery has been promoted as a promising tool for the detection of alien trees over large landscapes, but as yet there have been few attempts to assess its reliability for monitoring invasions. In particular, studies have not yet examined how well aerial imagery can detect densities of trees across invasions. To evaluate this, we used freely available, high-resolution aerial imagery to examine how age structure, spatial patterns and density of alien conifers varied across an invasion front. Overall, we were able to detect both the spatial pattern and distribution of trees with canopy diameters greater than 2.5 m, but we could only detect smaller trees with certainty where they were found at low density. These results point to aerial imagery being suitable for detecting trees at the edge of the invasion front, where they are often small and at low density. While assessments of the overall age-structure will underestimate the number of small trees, the number and spatial pattern of larger reproductive individuals can still be adequately captured. Thus low-cost aerial imagery can inform managers of where best to target control efforts at the invasion edge and also the location of large reproductive trees that are likely to contribute to future population expansion.
KeywordsExotic Non-native Orthophotography Size threshold Spatial patterns Wilding pine
The authors would like to thank Land Information New Zealand (LINZ) for providing the aerial imagery. We also are grateful to Nick Ledgard and Gordon Baker for their permission to access the Mt Barker Forest. We are indebted to Johnathon and Brendon Ridden for their many hours of help with fieldwork. Finally, we are grateful to the Bio-Protection Research Centre at Lincoln University for its research support.
This work was supported by the Miss EL Hellaby Indigenous Grasslands Research Trust, New Zealand.
Compliance with ethical standards
Conflict of interest
The authors declare they have no conflicts of interest.
- Bivand R, Keitt T, Rowlingson B (2017) rgdal: Bindings for the geospatial data abstraction library. R package version 1.2-8Google Scholar
- Bradley BA, Mustard JF (2006) Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing. Ecol Appl 16:1132–1147. https://doi.org/10.1890/1051-0761(2006)016[1132:CTLDOA]2.0.CO;2 CrossRefGoogle Scholar
- Department of Conservation (2018) Methods of control for wilding conifers. https://www.doc.govt.nz/nature/pests-and-threats/common-weeds/wilding-conifers/methods-of-control/. Accessed 23 July 2018
- Department of Finance, Service and Innovation: Spatial Services NSW (2018) Mapping and Spatial Services: Information Sheet. http://spatialservices.finance.nsw.gov.au/mapping_and_imagery. Accessed 23 July 2018
- Department of Rural Development and Land Reform (2018) National aerial photography and imagery programme. http://www.ngi.gov.za/index.php/what-we-do/aerial-photography-and-imagery. Accessed 23 July 2018
- Froude VA (2011) Wilding conifers in New Zealand: status report. Pacific Eco-Logic Ltd, Report prepared for the Ministry of Agriculture and Forestry, New Zealand. Bay of Islands, New ZealandGoogle Scholar
- Hijmans RJ (2016) raster: Geographic data analysis and modeling. R package version 2.5-8Google Scholar
- Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical analysis and modelling of spatial point Patterns. Wiley, ChichesterGoogle Scholar
- Komura R, Kubo M, Muramoto K (2004) Delineation of tree crown in high resolution satellite image using circle expression and watershed algorithm. In: Geoscience and remote sensing symposium, 2004. IGARSS’04. IEEE, pp 1577–1580Google Scholar
- Lamar WR, McGraw JB, Warner TA (2005) Multitemporal censusing of a population of eastern hemlock (Tsuga canadensis L.) from remotely sensed imagery using an automated segmentation and reconciliation procedure. Remote Sens Environ 94:133–143. https://doi.org/10.1016/j.rse.2004.09.003 CrossRefGoogle Scholar
- Landgate Government of Western Australia (2018) Online Aerial Photography. https://www0.landgate.wa.gov.au/maps-and-imagery/imagery/aerial-photography/aerial. Accessed 23 July 2018
- Law R, Dieckmann U (2000) A dynamical system for neighborhoods in plant communities. Ecology 81:2137–2148. https://doi.org/10.1890/0012-9658(2000)081[2137:ADSFNI]2.0.CO;2 Google Scholar
- Mauck J, Brown K, Carswell Jr WJ (2016) The National Map—Orthoimagery. In: United States Geol. Surv. Fact Sheet 2009-3055. https://pubs.usgs.gov/fs/2009/3055/. Accessed 23 July 2018
- Ministry of Primary Industries (2014) The right tree in the right place: New Zealand wilding conifer management strategy 2015–2030. Report produced for the Ministry of Primary Industries, New Zealand. http://www.wildingconifers.org.nz/about-us/programme-2/the-national-wilding/. Accessed 2 July 2018
- Natural Resources Canada (2016) National Air Photo Library. http://www.nrcan.gc.ca/earth-sciences/geomatics/satellite-imagery-air-photos/9265. Accessed 23 July 2018
- OpenAerialMap (2018) The Open Collection of Aerial Imagery. https://openaerialmap.org/. Accessed 23 July 2018
- Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R N 5:1–21Google Scholar
- R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/. Accessed 25 July 2018